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IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426

A Review of different Routing Protocols for Ad

Hoc Mobile Wireless Networks

1Maninder singh,

2Varun Marwaha

1,2Electronics & Communication Engineering Department,

Indo Global College of Engineering, Punjab, India 1chahal3225@gmail.com, 2varun.acear@gmail.com

Abstract: In this paper we review basics of wireless

communication its features, characteristic, fundamental

,applications etc along with its area of research , scope of

improvement and prominent attributes. Wireless

communication is not aimed to just general communication

as in multimedia services but have found underlying use in

industrial application and is embedded with different

hardware equipment for robotic application . Current

wireless multimedia services , for example, may include

2G,3G and 4G cellular radios, Wi-Fi and Bluetooth

technologies. Another category of which use wireless

technique is in VANET i.e vehicular Ad hoc Networks

,iMANET i.e. internet based mobile adhoc networks

MANET i.e. mobile ad hoc network. We will also discuss

about active research topic mainly MANET in wireless such

as improving and achieving high data rates on given width

of channel, improving quality of signal by controlling and

limiting losses and errors produced as a result of scattering,

interference and multipath propagation.

Keywords: iMANET, Wi-Fi, 4G, Bluetooth, Ad hoc.

I. INTRODUCTION

Wireless communication is activity of conveying

information between two or more points or terminals

without having any physical connection between them i.e.

medium is either air or vaccum. In actual communication

takes place with help of electromagnetic waves and strength

and frequency of electromagnetic waves depends upon

number of factors as on distance between two points,

environmental factors etc.Claude chappe of france was first

to design practical system for communication purpose

called semaphore system, conveyed information through

visual singnals. Then era of wired communication came

Paul Schilling pioneer of electrical telegraph , Alexander

Graham Bell inventor of first practical telephone.

Giant leap in communication was in 1895 ,when Italian

inventor Guglielmo Marconi successful in making first radio

telegraph or wireless telegraph.in 1906 first WARC i.e.

world administrative radio conference was held in order to

coordinate different inventions in wireless technology. 1915

first wireless voice transmission between new York and san

Fransco took place. Marconi discovered short waves in

1920 , which got reflected back from ionosphere . Edwin H

Armstrong 1933 discovered frequency modulation. In 1946,

First interconnection of mobile users to public switched

telephone network (PSTN) was established. Major

reformation In wireless technology took place in 80‘s and

90‘s ,with introduction of Advanced Mobile Phone System

(AMPS) in 1983 deployed in US using duplex channels.

Followed by introduction of packet switched services i.e.

SMS and GPRS in early 90‘s along with SPREAD

SPECTRUM techniques like CDMA. Overview in this

portion we are going to take general idea of different

prominent wireless techniques.

2G

2g signify second generation wireless technology. 2g

working parameters are defined by European

Telecommunications Standards Institute (ETSI) i.e. protocol

, frequency range etc. it was followed by 2.5g which in

addition contained packet switching technology.

3G

3g signify third generation wireless technology. 3g provide

higher data rates as compare to 2g.It working technology is

spread spectrum mainly CDMA. Current 3G systems have

been established through ITU‘s project on International

MobileTelecommunications 2000 (IMT-2000).

4G

4g signify fourth generation wireless technology. 4g provide

higher data rates as compare to 3g.It is new technology and

deployed only in few counties due to backward

compatibility problems.Wimax (Worldwide Interoperability

for Microwave Access) is example of 4g technology .

These technologies are much familiar to everyone as

compare to technology like MANET , InVANET.

Let us discuss these technology briefly one by one .

MANET A mobile ad hoc network is abbreviate as

MANET. In this technology a temporary wireless network is

established which configure itself to perform particularly

task. Each device in a MANET is free to move

independently in any direction, and will therefore change its

links to other devices. Ex Bluetooth .

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InVANETIntelligent vehicular ad-hoc network (InVANET)

is another term for promoting vehicular networking.

InVANET technology is best example is Wi-Fi .

II. PERFORMANCE EVALUATION

Among given technologies MANET is most active topic for

research work considering scope of improvement in this

technology.

Clearly a MANET is wireless technique that can be

performed without having any pre-existing infrastructure in

which each node or terminal act as router. But to coordinate

or establish or rout link between two terminals we need to

follow some sort of instructions,rules i.e. protocols. A

wireless ad-hoc environment introduces many problems such

as mobility and limited bandwidth which makes routing

difficult.

Hence efficiency and accuracy of system can be determined

on basis of QoS i.e. quality of service parameters like

Throughput (It is the average rate of successful message

delivery over a communication channel), Delay ( It is a

time required for packets to reach to destination node from

source node) and Fairness (it defines channel utilization

by users).

There are three types of Ad hoc Routing protocols. They are

pro-active protocols, active protocols and hierarchical

protocols. For comparison purpose we will take the few

protocols from each type. They are Dynamic Source routing

Protocol (DSR), Destination Sequenced Distance Vector

(DSDV), Ad hoc on demand Distance vector protocol

(AODV) and Ad-hoc On-demand Multipath Distance Vector

Routing (AOMDV).

Let us view some graph showing performance of MANET

using different protocols.

Throughput using DSR i.e. Dynamic Source routing Protocol.

Comparison between DSR and AODV

III. METHODOLOGY

We can use some simulation software which can mimic real

life scenarios for performance evaluation. For example we

can use network simulator NS-2.34 for simulation

purpose.To compare different ad-hoc routing protocol, it is

best to use identical simulation environments for their

performance evaluation.

IV. CONCLUSION

In this paper we are concentrated on MANET As MANET is

active research topic and we can study it under different

conditions and various parameters to get more accurate

results. Future work will include deep study and analysis of

MANET parameters.

REFERENCES

1. Shaily Mittal Prabhjot Kaur ―Performance comparison of

AODV, DSR and ZRP Routing Protocols in Manets‖

International Conference on Advances in Computing, Control,

and Telecommunication Technologies, 2009.

2. Wireless Network Evolution: 2G to 3G Authors:Vijay Kumar

Garg Editors:Theodore S.Rappaport.

3. Data communications and networking - by Forouzan.

4. Digital Communication by Proakis, Prentice Hall.

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IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426

Design of Fast Reliable Brain Hemorrhage CT

Scan Image Segmentation Technique

Abhishek Thakur1, Rajesh Kumar

2, Amandeep Bath

3, Jitender Sharma

4

1,2,3,4Electronics & Communication Engineering Department,

Indo Global College of Engineering, Punjab, India 1abhithakur25@gmail.com, 2errajeshkumar2002@gmail.com,

3amandeep_batth@rediffmail.com, 4er_jitender2007@yahoo.co.in

Abstract- The aim of this paper is to develop a fast

and reliable segmentation method to segment the

haemorrhage region from brain CT images. To calculate area

of segmented hemorrhage region that could be useful for

physicians or researchers involved in the treatment or

investigation of intracranial brain haemorrhage. Thus

improving the machine generated automated results and

reducing the human effort for better segmentation and saving

vital time for the treatment of a patient.

Keywords- Magnetic resonance imaging (MRI) and

Computed tomography (CT) scan also known as CAT

(Computer Axial Tomography), IV‘s (Intravenous therapy).

I. INTRODUCTION

Magnetic resonance imaging (MRI) and Computed

tomography (CT) scan also known as CAT (Computer Axial

Tomography) scan, are the two main ways by which

physicians take a picture of brain. In CT scan, testing is fast

and results are quick and thus making it exceptionally

valuable when prompt diagnosis and treatment are critical.

CT scan can be taken while patient is hooked up to IV‘s

(Intravenous therapy) or other medical equipment, unlike

some other scanning methods. CT scans can disclose

hematomas, hemorrhages, and skull fractures and thus

providing exact information to neurologist, necessary for

deciding whether emergency treatment is required. An MRI

process can take about 30-45 minutes to complete while a CT

scan may only take 5 to 10 minutes. So, a severe hemorrhage

could kill patient in the time consumed to take pictures in

MRI machine. Further, in some situations a CT scan can

actually detect abnormalities more easily than an MRI like a

CT scan is good at detecting acute haemorrhage and

problems in bone, for example fractures. On the other hand,

an MRI is best at detecting small or subtle lesions. CT scans

deliver a relatively high dose of radiation to a patient in

comparison to other diagnostic tests. This is not usually a

problem for a single scan, but patients who need to undergo

repeated tests can be subjected to a significant level of

radiation, hence increasing their cancer risk.

MRI makes use of powerful magnetic fields and the

magnetic reaction of the body cells to construct cross-

sectional images is similar to CT scans. MRI does not use X-

rays, so it can be safer than CT if multiple imaging

sessions are expected. The variations of MRI technology can

also examine brain functioning and identify injuries which

are not visible in CT scans. But even the detail available

using MRI cannot detect mild concussions. In acute head

injury cases, MRI is not often used. MRI has some

drawbacks, although MRI images yield finer detail than CT

scans. Some drawbacks include it takes longer to perform, it

is not as readily available as a CT scanner in most hospitals,

it is not practical for patients hooked up to medical

equipment and it cannot be used if patient has metal

embedded anywhere in the body. The greatest danger of an

MRI is to those with metal in their bodies that could be

moved around or heated up by the powerful magnetic force

created by MRI machine. MRI scans also require that a

patient stay very still for a long period of time, which may be

difficult if a patient is confused or fidgety. Each type of scan

is susceptible to different kinds of artefact i.e. blurring of the

image.

The definitive tool for accurate diagnosis of an

intracranial hemorrhage is CT scan i.e. computed

tomography as shown in fig1.1. Typically computed

tomography scanning of head is used to detect infarction,

tumors, calcifications, hemorrhage and bone trauma. Head

CT is the mainstay of diagnosis in ICH. Acute bleeding

appears hyper dense (whiter) on a CT, relative to the

surrounding tissues as shown in figure:

Fig.1 CT scan of a spontaneous intracranial hemorrhage

Image segmentation is the process of partitioning an

image into different segments. These segments often

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correspond to different tissue classes, organs, pathologies, or

other biologically relevant structures in medical imaging. In

medical image analysis, one fundamental problem is image

segmentation which identifies the boundaries of objects such

as organs or abnormal regions like tumors in images. Due to

noise, low contrast and other imaging ambiguities medical

image segmentation becomes difficult. It is possible with the

segmentation results to have shape analysis, detecting

volume change, and making a precise radiation therapy

treatment plan. Segmentation is a low-level operation, which

is necessary in order to perform high-level operations like

analysis of shape and size of the organs, 3-D volume

visualization and other such operations. In image processing,

image segmentation techniques are considered a critical

operation because further process steps have to rely on the

segmentation results. Two principal ways exists in order to

perform segmentation. First one is manual segmentation

which is performed by medical experts. In this case medical

expert has to manually outline region of interest using a

pointer device, usually mouse. Another way is to perform as

much as possible of the segmentation automatically and the

whole process is performed by means of a segmentation

algorithm with minimal user interaction.

Some of the segmentation techniques include shape

based segmentation and interactive segmentation. In shape

based segmentation many methods parameterize a template

shape for a given structure, often relying on control points

along the boundary. The entire shape is then deformed to

match a new image. Two of the most common shape-based

techniques are active shape models and active appearance

models. These methods have been very influential and have

given rise to similar models. On the other hand, interactive

methods are useful when clinicians can provide some

information like a seed region or rough outline of the region

to segment. Further, an algorithm can then iteratively refine

such segmentation with or without guidance from the

clinician. Manual segmentation, using tools such as a paint

brush to explicitly define the tissue class of each pixel,

remains the gold standard for many imaging applications

such as radio and telecommunications.

II. TECHNIQUES FOR IMAGE

SEGMENTATION

Approaches of image segmentation can be classified

according to both the features and the type of techniques

used. The features include pixel intensities, edge information,

and texture etc. There exist several common approaches on

medical image segmentation. However, multiple techniques

are often used in conjunction with one another for solving

different segmentation problems.

1. Thresholding methods:

The Segmentation algorithms are based on one of two

basic properties of intensity values discontinuity and

similarity. Discontinuity includes partitioning an image based

on abrupt changes in intensity, such as edges in an image.

Similarity includes partitioning an image into regions that are

similar according to predefined criteria. Threshold

segmentation techniques can be grouped in different classes

which includes local techniques that are based on the local

properties of the pixels and their neighbourhoods. Another

are global techniques to segment an image on the basis of

information obtain globally such as by using image

histogram; global texture properties. Last one split merge and

growing techniques use both the notions of homogeneity and

geometrical proximity in order to obtain good segmentation

results [7].

The gray levels of pixels belonging to the object are

different from the gray levels of the pixels belonging to the

background in many applications of image processing.

Therefore, thresholding becomes a simple but effective tool

to separate objects from the background. Thresholding

operation outputs a binary image whose one state will

indicate the foreground objects while the complementary

state will correspond to the background. On the basis of an

application, the foreground can be represented by gray-level

0 i.e. black and the background by the highest luminance i.e.

255 in 8-bit images, or conversely the foreground by white

and the background by black. It is one of the important

approaches to image segmentation. Often an image histogram

is used to determine the best setting for the threshold. A

thresholded image is defined as:

g x,y = 1 if f(x,y)>T

0 if f(x,y)≤T

Below is an example of image on which threshold is applied.

Fig.2: Thresholding method a. Original CT scan brain image b.

Brain image after thresholding

Thresholding is a simple yet often effective means for

obtaining segmentation in images. Many times thresholding

is used as an initial step in a sequence of image processing

operations. Some of its main limitations includes that in its

simplest form only two classes are generated and it cannot be

applied to multi-channel images. Also, thresholding typically

does not take into account the spatial characteristics of an

image. Due to this it becomes sensitive to noise and intensity

inhomogeneities, which can occur in images like MRI. Due

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to both these artifacts the histogram of image is corrupted,

making separation more difficult. There are single and

multiple thresholds as shown:

Fig. 3: Gray level histograms that can be partitioned as a. single

threshold b. multiple thresholds

Thresholding can be viewed as:

T = T[ x, y, p(x, y), f(x, y) ]

Here, T stands for the threshold. f (x, y) is the gray value of

point (x, y) and p(x, y) denotes some local property of the

point like the average gray value of the neighborhood

centered on point (x, y) . Based on above equation

thresholding techniques can be mainly divided into global,

local, and dynamic thresholding techniques. When T = T [

f(x, y) ] then threshold is global. For T = T[ p(x, y), f(x, y) ]

threshold is local and when T = T[ x, y, p(x, y), f(x, y) ] then

threshold is dynamic or adaptive. There are a number of

global thresholding techniques like minimum thresholding,

Otsu, optimal thresholding, histogram concave analysis,

iterative thresholding, entropy-based thresholding and so on.

Similarly the Main local thresholding techniques are simple

statistical thresholding, 2-D entropy-based thresholding,

histogram-transformation thresholding etc.

2. Region growing methods:

Region growing method is region based image

segmentation. It involves the selection of initial seed points

therefore also a pixel-based image segmentation method.

This method examines neighbouring pixels of initial seed

points and then determines whether the pixel neighbours

should be added to the region. Basically it involves to start

from some pixels (seeds) representing distinct image regions

and to grow them, until they cover the entire image. This

method needs a rule describing a growth mechanism and a

rule checking the homogeneity of the regions after each

growth step. This method for extracting a region of the image

that is connected based on some predefined criteria that can

be based on intensity information and/or edges in the image.

The simplest form of region growing requires a seed point

that is manually selected by an operator, and extracts all

pixels connected to the initial seed with the same intensity

value. Same as thresholding, region growing is not often used

alone but within a set of image processing operations,

particularly for the delineation of small and simple structures

such as tumors and lesions. Its primary disadvantage is that it

requires manual interaction to obtain the seed point. Hence, a

seed must be planted for each region that needs to be

extracted. Figure showing region growing method effect is as

shown.

Fig. 4: Region growing method a. Original CT scan brain image b.

Image after region growing method applied on original image

The Split and merge algorithms are related to region growing

but do not require a seed point. Region growing techniques

can also be noise sensitive that causes extracted regions to

have holes or even become disconnected. The partial volume

effects can also cause separate regions to become connected.

Classifiers:

The classifier methods are pattern recognition techniques.

These methods seek to partition a feature space derived from

the image using data with known labels. Classifiers are

known as supervised methods because of requirement of

training data that are manually segmented and then used as

references for automatically segmenting new data. Number

of ways exists in which training data can be applied in

classifier methods. The nearest-neighbor classifier is a simple

classifier, where each pixel or voxel is classified in the same

class as the training datum with the closest intensity. A

generalization of this approach is k nearest neighbor (kNN)

classifier where the pixel is classified according to the

majority vote of the k closest training data. This classifier is

considered a nonparametric classifier as it makes no

underlying assumption about the statistical structure of the

data. Maximum likelihood (ML) or Bayes classifier is a

commonly-used parametric classifier.

3. Clustering methods:

Clustering algorithms essentially perform the same function

like classifier methods without the use of training data.

Therefore, they are termed unsupervised methods. To

compensate for the lack of training data, clustering methods

iterate between segmenting the image and characterizing the

properties of the each class. In other words, clustering

methods train themselves using the available data.

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Fig. 5: Colouring of squares into three clusters representing results

of cluster analysis.

Some typical cluster models include:

1. Connectivity models: Example is hierarchical clustering

that builds models based on distance connectivity.

2. Centroid models: Example for these models includes the k-

means algorithm which represents each cluster by a single

mean vector.

3. Distribution models: In this, clusters are modelled using

statistical distributions, such as multivariate normal

distributions used by the Expectation-maximization

algorithm (EM).

The commonly used clustering algorithms are the K-means

or ISODATA algorithm, the fuzzy c-means algorithm and the

expectation-maximization algorithm. The K-means clustering

algorithm clusters data by iteratively computing a mean

intensity for each class and segmenting the image by

classifying each pixel in the class with the closest mean. The

result of applying the K-means algorithm to a slice of a MR

brain image is shown in figure below. Cerebrospinal fluid,

gray matter and white matter regions are there.

Fig. 6: Segmentation of MRI brain image a. Original brain MRI b.

Segmentation using K-means algorithm

Clustering algorithms do not require training data, but they

do require an initial segmentation or initial parameters.

4. Markov random field models:

Markov random field modelling itself is not a segmentation

method but a statistical model which can be used within

segmentation methods. In medical imaging, they are typically

used to take into account the fact that most pixels belong to

the same class as their neighbouring pixels. Markov random

field are often incorporated into clustering segmentation

algorithms like the K-means algorithm under a Bayesian

prior model. These models have difficulty of proper selection

of the parameters controlling the strength of spatial

interactions. Too high a setting can result in an excessively

smooth segmentation and a loss of important structural

details. Further, computationally intensive algorithms are

required by MRF methods. But in spite of this, MRFs are

widely used not only to model segmentation classes, but also

to model intensity in homogeneities that can occur in MRI

images and texture properties.

Fig. 7: Segmentation of MRI brain image a. Original brain MRI b.

Segmentation using K-means algorithm c. Segmentation using K-

means algorithm with MRF prior.

5. Artificial neural networks:

Artificial Neural Networks are electronic models based on

the neural structure of the brain. ANN is capable of machine

learning and pattern recognition. These are presented as

systems of interconnected neurons that can compute values

from inputs by feeding information through the network.

Neural networks have been used to solve a wide variety of

tasks that are hard to solve using ordinary rule-based

programming, including computer vision. Neural network

based image segmentation techniques include supervised

techniques such as feed-forward neural network, Multilayer

perceptron and unsupervised techniques such as pulse

coupled neural network (PCNN).

Fig. 8: Circular nodes represent artificial neurons and arrows

represent input output connections from one neuron to other neurons

6. Deformable models:

Deformable models are physically motivated, model-based

techniques. These are used for delineating region boundaries

using closed parametric curves or surfaces that deform under

the influence of internal and external forces. To delineate an

object boundary in an image, a closed curve or surface must

first be placed near the desired boundary. It is then allowed to

undergo an iterative relaxation process. The internal forces

are computed from within the curve or surface to keep it

smooth throughout the deformation and external forces are

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usually derived from the image to drive the curve or surface

towards the desired feature of interest.

Advantages of deformable include their ability to directly

generate closed parametric curves or surfaces from images

and their incorporation of a smoothness constraint that

provides robustness to noise and spurious edges.

Disadvantage include requirement of manual interaction to

place an initial model and choose appropriate parameters.

The standard deformable models can also exhibit poor

convergence to concave boundaries. This difficulty can be

reduced to some extent through the use of pressure forces and

other modified external force model.

Atlas guided approaches:

For medical image segmentation atlas-guided approaches are

a powerful tool when a standard atlas or template is

available. The atlas is generated by compiling information on

the anatomy that requires segmenting and then used as a

reference frame for segmenting new images. The atlas-

guided approaches are similar to classifiers except they are

implemented in the spatial domain of the image rather than in

a feature space. The standard atlas-guided approach treats

segmentation as a registration problem. Firstly it finds a one

to one transformation that maps a pre-segmented atlas image

to the target image that requires segmenting. This process is

often known as atlas warping. These approaches have been

applied mainly in MR brain imaging. Advantages include

that labels are transferred as well as the segmentation.

III. RESULTS AND DISCUSSION

In this paper, algorithm for segmentation and area calculation

of intracranial brain hemorrhage from CT scan images has

been implemented in matlab R2011b. All the images has

dimension of 512 x 512. Tests were performed on 11 human

brain CT scan hemorrhage images. The segmentation results

are as shown in figures below. The percentage of correct

classification (PCC) and computational time results are also

shown in table 4.

a) Original brain hemorrhage CT scan:

The original image of brain hemorrhage CT scan is

showing hemorrhage region which is appearing white in the

center. Grey matter is the brain region. The outermost white

region surrounding gray matter is skull.

b) Binary image

The binary image has pixel values in 1‘s and 0‘s. The

white region pixels correspond to 1‘s and black region pixels

correspond to 0‘s.

c) Binary image after bwlabel

Binary image after bwlabel morphological operation is

shown. bwlabel has syntax : [L,num] = bwlabel(f,conn)

which returns a matrix L, of the same size as BW, containing

labels for the connected objects in BW.

Fig. 8: a. Original brain hemorrhage CT scan, b. Binary image, c.

Binary image after bwlabel, d. Skull portion Binary image, e. Holes

filled image, f. Image after logical operations, g. Image in which

extra pixels are removed by morphological operation, g. Image in

which extra pixels are removed by morphological operation, h.

Extracted ROI image (Intracranial), i. Sobel edge operator

segmented image.

conn can have a value of either 4 or 8, where 4 specifies 4-

connected objects and 8 specifies 8-connected objects. L is

the label matrix, num gives total number of connected

components and is optional. f is input binary image and

conn parameter has default value of 8. The elements of L are

integer values greater than or equal to 0. The pixels labelled 0

are the background. The pixels labelled 1 make up one

object, the pixels labelled 2 make up a second object, and so

on.

d) Skull portion Binary image:

The skull portion comprises of white pixels i.e. 1‘s. Basically

this skull portion has to be removed in order to get only

intracranial region (ROI).

e) Holes filled image:

Holes filling is done using imfill morphological operation.

imfill fills image regions and holes. Image after holes filling

is shown in e. It has syntax BW2 = imfill(BW,'holes') which

fills holes in the binary image BW. Basically, a hole is a set

of background pixels that cannot be reached by filling in the

background from the edge of the image.

f) Image after logical operations

Image after logical operations & and ~ is shown. &

returns 1 for every element location that is true means

nonzero in both arrays and 0 for all other elements. ~

complements each element of the input array.

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g) Image in which extra pixels are removed by

morphological operation

The small objects in the image background are removed by

bwareaopen morphological operation. bwareaopen remove

small objects from binary image. It has syntax: BW2 =

bwareaopen(BW, P). It removes from a binary image all

connected components (objects) that have fewer

than P pixels, producing another binary image, BW2. This

operation is known as an area opening.

h) Extracted ROI image (Intracranial):

Extracted ROI image

The extracted ROI image is shown with removed background

pixels and skull portion. In other words, the image is left with

ROI (intracranial) portion and the segmentation is applied on

this image further.

IV. CONCLUSION AND FUTURE WORK

In this work sub-blocking rule based criteria is used for

intracranial hemorrhage segmentation in CT brain images

and also hemorrhage area is calculated. The CT scan brain

images with hemorrhage (ICH) are taken for segmentation.

This segmentation method used has main advantage of fast

the processing speed and better results for percentage of

correct classification with less noise in processed images.

Except one case, in rest all cases, the algorithm segments

hemorrhage. However, in some cases the segmentation is not

completely exact. Practically, the issue is very difficult of

avoiding and until now, a perfect automatic segmentation

algorithm does not exist. For such a reason, to analyse the

performance of algorithm, a comparison with manual

segmentation (ground truth) is done. The achievement of

better results lies in the use of sub-blocking rule based

criteria for the segmentation rather than multilevel otsu

thresholding method. The Otsu thresholding does not claim

to be the best automatic thresholding ever and can be

extended to a multi-level thresholding which results in

segmentation. Thresholding is a technique often applied to

image segmentation with a basic objective to classify the

pixels of a given image into two classes i.e. those pertaining

to an object and those pertaining to the background. In case

of an image with clear objects in the background, the bi-level

thresholding method can easily divide the object from the

background. On the other hand, to segment complex images

a multilevel threshold method is required. The multilevel

thresholding method segments the pixels into several distinct

groups in which the pixels of the same group have gray

levels within a specific range. However, when the

thresholding method is extended to multi-level thresholding,

the computation time grows exponentially with the number

of thresholds. Comparison of Sub-blocking rule based criteria

and MLSA (Multi-level local segmentation approach) in

terms of average computational time and average PCC is

shown in table 1.

Table1. Comparison of Sub-blocking rule based criteria and MLSA

(Multi-level local segmentation approach) in terms of average

computational time and average PCC

Methods Average

Computational

Time (seconds)

Average

PCC

(%)

Sub blocking rule

based ctiteria

0.10 0.986

MLSA 0.17 0.971

The Sub-blocking rule based criteria include k-means

clustering in addition to sobel edge and weighed sum

method. The MLSA (Multi-level local segmentation

approach) includes multilevel otsu thresholding method.

Table6. shows that obtained results for proposed algorithm

are better in comparison to MLSA method.

The future work will focus to further improve the results

using more image datasets of medical images and other

robust image segmentation techniques. A combination of

different methods may be applied to obtain a complete

effective and robust solution for segmentation. By using the

advantage of each method the segmentation results can be

improved.

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[2] Cerebrovascular Disease: Revised Imaging Guidelines from the American College of Radiology; Available at: http://www.eradimaging.com/ site/article.cfm?ID=779.

[3] Evaluation of Image Segmentation; Simon K. Warfield, Ph.D.;

Computational Radiology Laboratory Harvard Medical

School; Available at:

http://www2.imm.dtu.dk/projects/sparse/iceland-warfield-eval-

segmentation.pdf , pp. 1-46

[4] Morphological Image Processing Approach On The Detection

Of Tumor And Cancer Cells; Ms. M. Parisa Beham,

Ms.A.B.Gurulakshmi; IEEE 2012

[5] Comprehensive Applying Watershed Algorithm in Segmentation

of CT Brain Images; ZHU Bing-li, Xiong Jiang, Tan Xiao-ling;

2011 IEEE, pp. 81-83

[6] Intracranial Hemorrhage Annotation for CT Brain Images; Tong

Hau Lee, Mohammad Faizal Ahmad Fauzi , Su-Cheng Haw;

Proceeding of the International Conference on Advanced

Science, Engineering and Information Technology 2011, pp.

689-693

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 9

[7] A novel intuitionistic fuzzy approach for tumour/hemorrhage

detection in medical images; Tamalika Chaira and Sneh

Anand; Journal of Scientific & Industrial Research Vol. 70,

June 2011, pp. 427-434

[8] Hematoma volume detection and estimation from CT images; V.

SĂCELEANU, R. BRAD, A. BARGLAZAN, M. PEREANU;

AMT, vol. II, nr. 3, 2011, pp. 298-301

[9] Qualitative and Quantitative Comparisons of Haemorrhage

Intracranial Segmentation in CT Brain Images; W. Mimi Diyana

W. Zaki; Tencon 2011, pp. 367- 373

[10] An Algorithm for Automatic Segmentation of Spontaneous

Cerebral Hemorrhages; R. Rodríguez Morales; Claib 2011, pp.

1-4

[11] Comparative Study of Adaptive Network-Based Fuzzy

Inference System (ANFIS), k-Nearest Neighbors (k-NN) and

Fuzzy c-Means (FCM) for Brain Abnormalities Segmentation;

Noor Elaiza Abdul Khalid, Shafaf Ibrahim, Mazani Manaf;

INTERNATIONAL JOURNAL OF COMPUTERS Issue 4,

Volume 5, 2011, pp. 513- 524

[12] Medical Image Segmentation Based on Contourlet Transform

and Watershed Algorithm; Hongying LIU, Yi LIU, Qian LI,

Hongyan LIU, Yongan TONG; 2011 IEEE, pp. 224-227

[13] A Novel Anatomical Structure Segmentation Method of CT

Head Images; Xiaojun Zang, Jian Yang, Dongdong Weng, Vue

Liu, Yongtian Wang; The 2010 IEEE/ICME International

Conference on Complex Medical Engineering July 13-15,2010,

Gold Coast, Australia, pp. 316-320

[14] A novel method of CT brain images segmentation; Xiaojun

Zang, Yongtian Wang, Jian Yang, Yue Liu; 2010 International

Conference of Medical Image Analysis and Clinical

Application (MIACA), pp. 109-112

[15] Multi-dimensional Data Analysis of Intracerebral Hemorrhage

from CT Images; Jianmin Dong, Fangxia Shi; 2010 3rd

International Conference on Biomedical Engineering and

Informatics (BMEI 2010), pp. 406-409

[16] Fuzzy expert system for edema segmentation; Sven Lencaric;

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Volume 5, 2010, pp. 311- 317

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Medical Image Segmentation; Arpit Srivastava, Abhinav Asati,

Mahua Bhattacharya; 2010 IEEE International Conference on

Bioinformatics and Biomedicine, pp. 416-421

AUTHORS

First Author– Abhishek Thakur: M.

Tech. in Electronics and

Communication Engineering from

Punjab Technical University, MBA in

Information Technology from

Symbiosis Pune, M.H. Bachelor in

Engineering (B.E.- Electronics) from

Shivaji University Kolhapur, M.H. Five years of work

experience in teaching and one year of work experience in

industry. Area of interest: Digital Image and Speech

Processing, Antenna Design and Wireless Communication.

International Publication: 7, National Conferences and

Publication: 6, Book Published: 4 (Microprocessor and

Assembly Language Programming, Microprocessor and

Microcontroller, Digital Communication and Wireless

Communication). Working with Indo Global College of

Engineering Abhipur, Mohali, P.B. since 2011.

Email: abhithakur25@gmail.com

Second Author – Rajesh Kumar is

working as Associate Professor at

Indo Global College of Engineering,

Mohali, Punjab. He is pursuing Ph.D

from NIT, Hamirpur, H.P. and has

completed his M.Tech from GNE,

Ludhiana, India. He completed his

B.Tech from HCTM, Kaithal, India. He has 11 years of

academic experience. He has authored many research papers

in reputed International Journals, International and National

conferences. His areas of interest are VLSI, Microelectronics

and Image & Speech Processing.

Third Author – Amandeep Batth:

M. Tech. in Electronics and

Communication Engineering from

Punjab Technical University, MBA

in Human Resource Management

from Punjab Technical University ,

Bachelor in Technology (B-Tech.)

from Punjab Technical University . Six years of work

experience in teaching. Area of interest: Antenna Design and

Wireless Communication. International Publication: 1,

National Conferences and Publication: 4. Working with Indo

Global College of Engineering Abhipur, Mohali, P.B. since

2008.

Email: amandeep_batth@rediffmail.com

Fourth Author – Jitender Sharma: M. Tech. in Electronics

and Communication Engineering from Mullana University,

Ambala, Bachelor in Technology (B-Tech.)from Punjab

Technical University . Five years of work experience in

teaching. Area of interest:, Antenna Design and Wireless

Communication. International Publication: 1 National

Conferences and Publication:6 and Wireless

Communication). Working with Indo Global college since

2008.

E-mail: er_jitender2007@yahoo.co.in

International Journal of Electrical & Electronics Engineering 10 www.ijeee-apm.com

IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426

Design of Lambda/2 Dipole Antenna 1Amandeep Bath,

2Abhishek Thakur,

3Jitender Sharma ,

4Prof. Basudeo Prasad

1,2,3,4Electronics & Communication Engineering Department,

Indo Global College of Engineering, Punjab, India 1amandeep_batth@rediffmail.com, 2abhithakur25@gmail.com, 3er_jitender2007@yahoo.co.in

Abstract- Ultra wideband is a wireless technology to

realize high speed communications which is performed in

wideband. In this paper the wideband dipole antenna is

designed. The simulation is done using ANSOFT HFSS

simulation software.

Index Terms- Broad band, wide beam, circular polarization,

conducting wall, micro strip antenna, Wide-Band, Omni

directional radiation pattern smart grid, Wi Max directive

antennas, UWB antennas, Biotelemetry, capsule endoscope,

dipole antenna ,planar reflector antenna.

I. INTRODUCTION

In radio and telecommunications a dipole antenna also

known as doublet is the easiest and most commonly used

class of antenna. It is made up of two similar conductive

elements such as metal wires or rods which are generally

bilaterally symmetrical. The driving current from the

transmitter is given, or for receiving antennas the output

signal to the receiver is obtained and taken, between the two

halves of the antenna. Each side of the feedline to the

transmitter or receiver is joined to one of the conductors.

This is different with a monopole antenna, which is made up

of a single rod or conductor with one side of the feed line

joined to it, and the other side connected to some type of

ground. The best example of a dipole is the "rabbit ears"

television antenna which is found on broadcast television

sets.

The most common type of dipole is two straight rods or wires

which are connected end to end on the same axis, with the

feed line connected to the two adjacent ends. This is the

easiest type of antenna from a theoretical point of view.

Dipoles are resonating antennas, meaning that the elements

serve as resonating elements, with standing waves of radio

current which flows back and forth between their ends. So

the length of the dipole elements is calculated by the

wavelength of the radio waves used. The most common type

is the one half wave dipole, in which both of the two rod

elements is approximately 1/4 wavelength long, so the

complete antenna is a half-wavelength long. Numerous

different types of the dipole are also used, such as the folded

dipole, short dipole, cage dipole, bow-tie, and batwing

antenna. Dipoles may be used as standalone antennas

themselves, but they are also used as feed antennas (driven

elements) in many more advanced antenna types, such as the

Yagi antenna, parabolic antenna, reflective array, turnstile

antenna, log periodic antenna, and phased array. The dipole

was the oldest and primitive type of antenna; it was invented

by German scientist Heinrich Hertz around 1886 in his

advanced research of radio waves

A dipole is a symmetrical antenna, as it is composed of

two symmetrical ungrounded elements. Therefore it works

best when fed by a balanced transmission line, such as a

ladder line. It happens because in that case the symmetry

(one aspect of the impedance complex, which is a complex

number) matches and therefore the power transfer is external.

When a dipole with an unbalanced feed line such as coaxial

cable which is generally used for transmitting the signal, the

shield side of the cable, in addition to the antenna, radiates.

RF currents are induced into other electronic equipment very

close to the radiating feed line, producing RF interference.

Furthermore, the efficiency of the antenna is very low

because it is radiating closer to the ground and its radiation as

well as the reception pattern may be asymmetrically

distorted. At very high frequencies, where the coax diameter

is generally more than the length of the dipole, this becomes

a more prominent problem. To remove this, dipoles fed by

coaxial cables have a balloon kind of structure between the

cable and the antenna, the unbalanced signal provided by the

coax is converted to a very balanced symmetrical signal for

the antenna.

Agile reconfigurable antennas for future communication

systems have attracted researchers around the globe.

Antenna's characteristics such as frequency, radiation pattern

and polarization are reconfigured to attain the demands for

agile radios. A lot of researches focus on frequency

reconfiguration as future communication systems such as

cognitive radio needs an antenna that can do spectrum

sensing and communication. In reconfigurable frequency

antennas development, recently a reconfigurable wide-band

to agile narrow frequencies, using a printed log periodic

dipole array antenna, was introduced. A wideband slotted

multifunctional reconfigurable frequency antenna for

WLAN, WIMAX, UWB and UMTS has been proposed in, a

frequency reconfigurable antenna, consisting of two

structures; one is an ultra-wide band (UWB) and other is a

frequency reconfigurable triangle shape antenna, is proposed

for cognitive radio communication

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 11

Ultra-wide band antennas have already been used in

areas such as satellite communication, remote sensing, and

ultra-wide band radar and so on. Currently, the wireless area

network (WLAN) in the 2.4-GHz (2.4-2.485 GHz) and 5-

GHz (5.l5-5.875 GHz) bands is the most popular networks

for accessing the internet the antenna for an AP not only

requires dual-band operation but also needs to have an

appropriate radiation profile in both bands, namely similar

gain, wide beam width, and high front-to-back ratio. Wireless

communications continues to enjoy exponential growth in

Industrial, Scientific, and Medical (ISM) band. The future

generation wireless networks require systems with broad-

band capabilities in various environments to satisfy several

applications as smart grid, personal communications, home,

car, and office networking .On the other hand, many modern

wireless communication systems such as radar, navigation,

satellite, and mobile applications use the circular polarized

(CP) radiation pattern. For the best UWB performance, the

transmitter and receiver (T/R) antennas should have flat and

high directive gain, narrow beam, low side and back lobes

over the operational frequency band; to attain the largest

dynamic range, best focused illumination area, lowest T/R

coupling, reduced ringing and uniformly shaped impulse

radiation.UWB has promised to offer high data rates at short

distances with low power, primarily due to wide resolution

bandwidth.

II. ANTENNA DESIGN AND CONFIGURATION

All The geometry and configuration of the proposed

antenna is shown in the figure. Initially the design properties

are selected by adjusting the local variables such as the

substrate height ‗l=25cm' and the radius 'a=0.5mm' and the

position as well. As shown in the figure the proposed antenna

consists of a cylindrical radiating substrate which is duplicate

d around the X axis with a rectangular lumped port excitation

between them. The duplicated substrate cylindrical antenna

element around the X axis is shown in the figure.

Fig. 1: Duplicated cylindrical substrate around the axis

Fig. 2: Rectangular radiating element between substrates

The material of the substrate is kept as pec with a bulk

conductivity of 1e+030 Siemens/m. The rectangular element

between the cylindrical substrates provides the lumped

excitation with a position 0,-.5,-2. The the integrating line is

drawn between the cylindrical substrates through the

rectangular element.

Fig. 3: Integrating line between the substrates

The structure is then covered by a vacuum box with the

position -100,-75,-75 mm and the other dimensions as

X=200, Y=150, Z=150mm. Also the transparency is adjusted

as 0.76. Further the faces of the vacuum box are individually

selected for assigning the radiation boundary. Before the

final validation check the solution frequency is adjusted as

300 MHz for the setup. Also for the same set up the

frequency sweep is adjusted by keeping the sweep type as

fast and the start and stop frequencies as 100 and 500Mhz

respectively by keeping the count as linear. Finally the design

undergoes the validation check for the errors.

Fig. 4: Air box over the dipole

III. DIPOLE CHARACTERISTICS

International Journal of Electrical & Electronics Engineering 12 www.ijeee-apm.com

A. Frequency versus length

Dipoles that are very small even smaller than the

wavelength of the signal are called Hertz an, short, or

infinitesimal dipoles. These have a very low radiation

resistance and a high capacitive reactance, so they are not

very much efficient; though inefficient, they can be practical

antennas for long wavelengths. Dipoles whose length is half

the wavelength of the signal are called half-wave dipoles, and

are more efficient. In general radio engineering, the term

dipole usually means a half-wave dipole (center-fed).A half-

wave dipole is cut to length l for frequency f in hertz

according to the formula

Where λd is the wavelength on the dipole elements, λ0 is

the free-space wavelength, c is the speed of light in free

space (299,792,458 meters per second (983,571,060 ft/s)),

and k is called adjustment factor. The adjustment factor

completely compensates for propagation speed being

somewhat less than the speed of light. The dipole elements

will have distributed inductance and capacitance. The value

of k is around 0.95. For thin wires with the dimensions

(radius = 0.000001 wavelengths), k is approximately 0.981; for thick wires (radius = 0.01 wavelengths), k drops to about

0.915.The above formula which is given is often shortened to

the length in meters = 143/MHz or the length in feet =

468/MHz; MHz is the frequency in megahertz.

A. Elementary doublet

From a theoretical point of view, the dipole antenna is

the simplest antenna. An elementary doublet or Hertzian

dipole as shown in the figure is a small length of conductor

δℓ (small compared to the wavelength λ) carrying an

alternating current whose equation is:

Fig. 5: Elementary doublet.

Here ω = 2πf is the angular frequency (and f the

frequency), and i = √−1 is the imaginary unit, so that I is a

phasor. It is used in, for example, analytical calculation on

more complex antenna geometries. Note that physical

construction of the dipole is difficult because the current

needs somewhere to come from and somewhere to go to.

Actually, this small length of conductor will be just one of

the multiple segments into which we must divide a real

antenna, in order to calculate its properties. In the case of the

elementary doublet which is shown in the figure it is possible

to find exact, closed-form expressions for its electric field, E,

and its magnetic field, H. In spherical coordinates, they are

where r is the distance from the doublet to the point

where the fields are evaluated, k = 2π/λ is the wave number,

and Z = √μ/ε = 1/εc = μc is the wave impedance of the

surrounding medium (usually air or vacuum) and the

concerned equations are also shown .The energy associated

with the term of the near field flows alternately out of and

back into the antenna. The exponent of e accounts for the

phase dependence of the electric field on time and the

distance from the dipole. Often one is interested in the

antenna's radiation pattern only in the far field, when

r ≫ λ/2π. In this regime, only the 1/r term contributes, and

hence. The concerned equations are

The far electric field, Eθ, of the electromagnetic wave is co-planar with the conductor and perpendicular with the line

joining the dipole to the point where the field is calculated. If

the dipole were placed in the center of a sphere with the axis

south-north, the electric field would be parallel to geographic meridians and the magnetic field of the electromagnetic wave

would be parallel to geographic parallels

B. Dipole antenna techniques

Implementation of wideband antenna for smart grid applications with a frequency bandwidth of 40% and gain of

3 to 4dbThe antenna design and simulation was carried out

using ANSYS‘ HFSS software which is the industry-standard

simulation tool for 3-D full-wave electromagnetic field

simulation. The total size of the antenna is 20mm x 10mm x

2mm. This new design offers a wide fractional frequency

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 13

bandwidth of about 40% with a gain from 3dB-4.3dB over

the frequency band (5GHz – 7.5GHz)

Using ultra wideband dipole antenna operating at 1.75 to 40

GHz .It is shown that the proposed antenna works well in

1.7GHz-40GHz frequency range and the main direction of

the radiation pattern keeps stable during the whole frequency range. The H plane demonstrates an excellent Omni-

directional pattern.

A Dual-band Wide-beam width WLAN Access Point

Antenna with similar gain and wide beam width in both the

2.4- and 5-GHz WLAN bands. This paper describes a dual

band printed dipole antenna that has nearly identical radiation

patterns with similar gain and wide beam width in both the

2.4- and 5-GHz WLAN bands. The proposed design employs

two techniques to improve the radiation pattern. These

techniques are the use of an angle dipole and vertical copper

plates arranged on the ground plane for improvement in the

radiation pattern of lower and upper bands, respectively

.Ultra band dipole antenna and circularly polarized antenna

provides the best Omni directional radiation pattern. Also the

techniques such as angled dipole and vertical copper plates

on ground plane are used for the further improvement of the

radiation pattern of the antenna.

IV. RESULTS AND DISCUSSION

In this section the lambda /2 dipole antenna is

constructed and the numerical and experimental results

regarding the radiation characteristics are presented and

discussed. The simulated results are obtained by using the An

soft simulation software high frequency structure simulator.

The measured and simulated characteristics of the antenna

are shown and from the far field report the rectangular plot,

the 3D polar plot and are drawn and the radiation

characteristics are also plotted.

Fig. 6: XY Rectangular Plot

Fig. 7: 3D Polar Plot

Unlike other antennas reported in the literature to date, the

proposed antenna displays a good omnidirectional radiation

pattern even at higher frequencies. The designed antenna has

a small size and good return loss and radiation pattern

characteristics are obtained in the frequency band of interest.

The simulated and experimental results show that the

proposed antenna could be a good candidate for UWB

applications. The radiation pattern is shown in the figure for

the dipole antenna.

Fig. 8: Radiation Pattern

Next the radiation pattern for a half wave dipole antenna is

shown along with the stacked XY plot

Fig. 9: XY stacked plot

International Journal of Electrical & Electronics Engineering 14 www.ijeee-apm.com

Fig. 10: Electric fields (blue) and magnetic fields (red) radiated by a

dipole antenna

A. Radiation Pattern and Gain

Dipoles have a radiation pattern, shaped like a toroids

(doughnut) symmetrical about the axis of the dipole. The

radiation is maximum at right angles to the dipole, dropping

off to zero on the antenna's axis. The theoretical maximum

gain of a Hertzian dipole is 10 log 1.5 or 1.76 dBi. The maximum theoretical gain of a λ/2-dipole is 10 log 1.64 or

2.15 dBi.

V. CONCLUSION AND FUTURE WORK

With the rapid progress of wireless technology in recent

years, various wireless systems such as GSM,

WCDMA/UMTS, Bluetooth, WLANs, and GPS have been

highly integrated into the mobile devices, and in order to

fulfill the RF system requirements using the different

frequency band, antenna technology is required to wideband

characteristics .On the other hand, many modern wireless

communication systems such as radar, navigation, satellite,

and mobile applications use the circular polarized (CP)

radiation pattern. The attractive advantages of the CP antenna

are existed as follows. Firstly, since the CP antennas send

and receive in all planes, it is strong for the reflection and

absorption of the radio signal. In the multi-path fading

channel environment, the CP antenna overcomes out of phase

problem which can cause dead-spots, decreased throughput,

reduced overall system performance. Additionally. Also

further improvements could be done by using antenna

substrates with higher dielectric constants in order to reduce

the size a broad band wide beam circular polarization micro

strip antenna. The configuration of the antenna is simple and

easy to fabricate compared with conventional micro strip

antenna, the radiation beam is broadened obviously. Further

research on circularly polarized wideband micro strip

antenna is required as it gives the best performance and

overall improvement of antenna parameters.

REFERENCES

[1] Gaboardi P., Rosa L., Cucinotta A., and Selleri S., ―Patch Array Antenna for UWB Radar Applications‖, in 3rdEuropean RadarConference, 2006, p.281-284.

[2] Yoann Letestu and Ala Sharaiha, ―Size reduced multi-band printed quadrifilar helical antenna,‖ IEEE Trans. Antennas Propag., vol. 59, pp. 3138-3143, 2011.

[3] A. Siligaris et al., ―A 65-nm CMOS fully integrated transceiver module for 60-GHz Wireless HD applications,‖

IEEE Journal of Solid-State Circuits, vol. 46, no. 12, pp. 3005-3017, Dec. 2011.

[4] S. Manafi, S. Nikmehr, and M. Bemani, "Planar reconfigurable multifunctional antennaforWLAN/wimax/UWB/pcsdcs/UMTS applications," Progress In Electromagnetics Research C, Vol.26, 123- 137, 2012.

[5] C. R. Medeiros, E. B. Lima, 1. R. Costa, and C. A.Fernandes,

"Wideband slot antenna for WLAN accesspoint, " IEEE

Antenna Wireless Propagate. Lett., vol. 9,pp. 79-82,2010.

[6] F. Ghanem, P. S. Hall and J. R. Kelly, ―Two port frequency

reconfigurable antenna for cognitive radios‖, Electronics

Letters,vol.45, 2009,pp.534-536.

[7] E. Ebrahimi, J. R. Kelly and P. S. Hall, ―A reconfigurable

Narrowband antenna integrated with wideband monopole for

cognitive radio applications‖, IEEE Antennas and

Propagation Society International Symposium( APSURSI),

2009.

[8] J. W. Baik, S. Pyo, T.H. Lee, and Y.S. Kim, ―Switchable

printed Yagi- Uda antenna with pattern reconfiguration‖,

ETRI Journal, vol.31 2009,pp.318-320

[9] M. Sanad, "A Small Size Micro strip Antenna Circuit", IEEE

International Conference on Antenna and Propagation, vol. 1,

pp. 465-471, April1995.

[10] P. Suraj and V. R. Gupta, ―Analysis of a Rectangular

Monopole Patch Antenna‖ ‗International Journal of Recent

Trends in Engineering,Vol. 2, No. 5, pp. 106-109, November

2009.

[11] M. N. Srifi, M. Meloui and M. Essaaidi, ―Rectangular Slotted

Patch Antenna for 5-6GHz Applications‖, International Journal

of Microwave and Optical Technology, Vol.5 No. 2, pp., 52-57

March 2010.

[12] Ansoft Corporations, HFSS V.12- Software based on the

finite element method [13] G. Augustin, S. V. Shynu, C. K.

Aanandan, and K. Vasudevan, "Compact dual-band antenna for

wireless access point, " Electron. Lett., vol. 42, no. 9, pp. 502-

503, Apr. 2006.

AUTHORS

First Author – Amandeep Batth:

M. Tech. in Electronics and

Communication Engineering from

Punjab Technical University,

MBA in Human Resource

Management from Punjab

Technical University , Bachelor in

Technology (B-Tech.) from Punjab Technical University .

Six years of work experience in teaching. Area of interest:

Antenna Design and Wireless Communication. International

Publication: 1, National Conferences and Publication: 4.

Working with Indo Global College of Engineering Abhipur,

Mohali, P.B. since 2008.

Email: amandeep_batth@rediffmail.com

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 15

Second Author– Abhishek

Thakur: M. Tech. in Electronics

and Communication Engineering

from Punjab Technical University,

MBA in Information Technology

from Symbiosis Pune, M.H.

Bachelor in Engineering (B.E.-

Electronics) from Shivaji

University Kolhapur, M.H. Five years of work experience in

teaching and one year of work experience in industry. Area

of interest: Digital Image and Speech Processing, Antenna

Design and Wireless Communication. International

Publication: 7, National Conferences and Publication: 6,

Book Published: 4 (Microprocessor and Assembly Language

Programming, Microprocessor and Microcontroller, Digital

Communication and Wireless Communication). Working

with Indo Global College of Engineering Abhipur, Mohali,

P.B. since 2011.

Email: abhithakur25@gmail.com

Third Author – Jitender Sharma: M. Tech. in Electronics

and Communication Engineering from Mullana University,

Ambala, Bachelor in Technology (B-Tech.)from Punjab

Technical University . Five years of work experience in

teaching. Area of interest:, Antenna Design and Wireless

Communication. International Publication: 1 National

Conferences and Publication:6 and Wireless

Communication). Working with Indo Global college since

2008.

E-mail:er_jitender2007@yahoo.in

International Journal of Electrical & Electronics Engineering 16 www.ijeee-apm.com

IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426

Speech Recognition Based Wireless Automation

of Home Appliances for Disabled Persons

Abhishek Thakur1, Rajesh Kumar

2, Amandeep Bath

3, Jitender Sharma

4

1,2,3,4Electronics & Communication Engineering Department,

Indo Global College of Engineering, Punjab, India 1abhithakur25@gmail.com,

2errajeshkumar2002@gmail.com,

3amandeep_batth@rediffmail.com,

4er_jitender2007@yahoo.co.in

Abstract- Matlab straight forward programming interface

make it an ideal tool for Hindi Key word Recognition. For

the extraction of the feature, Hindi Key word database has

been designed by using the Matlab 7.5. The database

consists of the eight key words. Each key word has been

stored in database by the ten speakers, eight male speakers

and two female speakers consist of total 80 samples for eight

commands. Features of the speech signal which are extracted in the form of MFCC coefficients and Dynamic Time

Warping (DTW) has been used as features matching

techniques. This thesis presents the technique to detect

utterance using end point detection, MFCC to extract

features and DTW to compare the test patterns. The

recognition results are tested for clean and noisy test data.

The system can be said to be robust as average accuracy for

clean data is 97.50 % while that for noisy data is 91.25 % or

above is acceptable since most people would not mind

repeating a command to the system one out of ten times or

less. The system can be implemented using one of the common microcontrollers with a small amount of dedicated

memory and an analog to digital converter to accept the

input speech. The system would be fast, small and cost

efficient to be incorporated into a wide variety of consumer

electronics. The aim of this thesis is therefore to develop a

speaker dependent, isolated word, limited vocabulary speech

recognition system that is small enough to fit in a small

household appliance and that can be operated in real time.

Index Terms- Automatic Speech Recognition (ASR), Mel

Frequency Cepstral Coefficient (MFCC) and Dynamic Time

Wrapping (DTW)

V. INTRODUCTION

Although many systems exist for speech recognition, none of

them address the needs for consumer level applications. In

order for a system to be incorporated in the everyday needs

of a consumer, the system must be speaker independent, fast,

low cost, require no training and small enough to be fit

inside a consumer appliance. Such a system will move speech recognition from the domain of the academic or

industrial application to that of a common home user. The

above system can be implemented using current technology

once a certain number of compromises are made. For

example, let's say a speech recognition system is to be

developed so that it can be incorporated into a home

microwave oven. One can immediately see that there is no

need to have a 60,000 word vocabulary for such a system, a

dozen words including the digits are sufficient for its

operation. The system could be further simplified if one does

not allow the user to change the number of words in the

vocabulary. The Second aspect of the system is that it does

not have to accept continuous speech. For example, a

common command may be "Move.... Forward.... Fast....

Start. Proposed design for home automation system and

Matlab based Hindi key word speech recognition system is

for disabled persons, as they are not able to move from one

place to other and can‘t locate switches. This paper attempt to provide them solution, by sitting on wheel chair or bed

they can switch on and off home appliances and also control

internal parameters like wheel chair direction, fan speed,

heater temperature. Physically challenged persons find

difficulty in power ON/OFF their home loads such as fan,

light, AC etc. they require an attendee to do these things. In

the absence of the attendee their world seems to be more

difficult. This design helps the person with physical

disability and elderly to navigate easily within their home in

a wheelchair by giving voice commands. [3-5] designed for

navigation of robot and forklift by giving voice commands. Some of the voice based design uses a voice recognition chip

with integrated or interfaced memory chip that has a

drawback of having limited number of voice commands. The

reported design Speech Recognition Based Wireless

Automation of Home Appliances for Disabled Persons

involves automation home loads by giving voice commands

in a wireless environment.

VI. SYSTEM OVERVIEW

This paper is related to the controlling of the

electronic/electrical equipment using voice key words. In

this paper we are going to recognize the Hindi key word of

the person and control the desired parameter. The goal of the thesis is to help the disabled and handicapped persons, who

are not able to locate switches or, not able to reach there.

This thesis can also work as a security purpose by operating

the machines through the voice and will be operated by only

one person. This can also work for the home atomization and

replace the switches and remotes by the voice command.

This is done using software designed in Matlab 7.5 using

MFCC and DTW. By using this software Hindi key words

spoken in real time will match with pre recorded samples

and generate ASCII code. These ASCII codes send to

microcontroller using serial communication RS-232. All peripherals are controlled by the microcontroller. The output

of the microcontroller controls the various applications upon

receiving the input from the software. The relays are

controlled on the ports of microcontroller to activate a

particular appliance connected to the particular port.

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 17

Fig. 1: Microcontroller Interfacing.

Automatic speech recognition system and home

automation system port connection with external peripherals

is shown in Table 1. All peripheral are connected to

corresponding port pin of microcontroller (89C52) as given in Table 1. These peripherals work according to our program

and discussed in software design section. When command

word given by user through microphone it is recognized by

proposed algorithm and ASCII code will be generated. These

ASCII code given to 89C52 microcontroller, if recognized

code match then appliance will perform particular operation

related to that key word.

TABLE 1: MICROCONTROLLER PORT CONNECTION

S.N. Ports of 89C52

µc

Hardware Devices

Control

Hindi Key

Word

1 P1.0, P1.1, P1.2 ADC BAND

2 P1.4 Temperature 30 deg. TIESH

3 P1.5 Temperature 50 deg. PACHAS

4 P1.6 Go AAGE

5 P1.7 Reverse PICHE

6 P1.6, P1.7 Break RUKO

7 P2.2 Fan low set DHEERE

8 P2.3 Fan medium set TEJ

As we can see in table 1, if AAGE key word recognized

then Port 1.7 goes logic one and Port 1.6 goes logic zero.

Which means that robot moves in forward direction. The logic one and logic zero position of the port is shows in table

1 for corresponding key word.

VII. HARDWARE DESIGN

a) Voice processor:

Next stage is voice processor stage consisting of .m

voice processor file. After comparison in voice processor

data is send to microcontroller for control or driving action,

we are using RS232 as application communication protocol.

The whole process goes in the following manner e.g. if we

say AAGE key word the action related to ―AAGE key word‖

has to performed and if we say ―PICHE key word‖ then the action related to PICHE key word has to be performed. As

shown in figure 2 when we say key any word the

microphone takes analog signal and converts it to the

electrical signal then attenuation of the signal is performed

by the attenuator. Attenuated signal is transferred to the voice processor, these files are executed and an ASCII code

is then transferred to the microcontroller through the RS 232

standard communication protocol. In this manner the voice

will hold the control action of the machine or the electric

appliance.

Fig. 2: Speaker recognition process.

b) Temperature sensor circuit:

We can use wide range of supply voltages lies between

single supply 3 V to 30 V (LM2902 and LM2902Q 3V to

26V), or Dual supplies. Common mode input voltage range

includes ground that allow direct sensing to near ground.

The low supply current drain is independent to the supply

voltage 0.8 mA Typ. Low input bias and offset parameters

includes input offset voltage 3 mV Typ. input offset current

2 nA Typ. input bias Current 20 nA Typ. differential input

voltage range equal to maximum rated supply voltage 32 V open loop differential voltage amplification 100 V/mV Typ.

Fig. 3: LM 35 Interface.

c) Analog to digital converter:

Analog to digital converter device is a high current four

channel driver designed to accept standard DTL or TTL

logic levels, monolithic integrated high voltage and drive inductive loads (such as relays solenoids, DC and stepping

motors) and switching power transistors. To simplify use as

two bridges each pair of channels is equipped with an enable

input. A separate supply input is provided for the logic,

allowing operation at a lower voltage and internal clamp

diodes are included. This device is suitable for use in

switching applications at frequencies up to 5 kHz. The

L293D is assembled in a 16 lead plastic package which has 4

center pins connected together and used for heat sinking.

The L293DD is assembled in a 20 lead surface mount which

has 8 center pins connected together and used for heat

sinking. 600Milli amperes output current capability per channel, 1.2A peak output current per channel, enable

facility, over temperature protection, logical input voltage up

to 1.5 V, internal clamp diodes.

International Journal of Electrical & Electronics Engineering 18 www.ijeee-apm.com

Fig. 4: Functional block diagram of A to D converter.

d) Building a wireless remote control:

Now question arises that how you can get rid of that

long wired tail dangling out of your remote control robot?

Well, transforming your wired remote control into a wireless one isn‘t as difficult as you may think. The easiest solution

would be to hack those cheap wireless toy cars, take their

electronic guts out and use them in your robot. But if you

want more flexibility, you can build a custom remote control

system. The idea is to use off the shelf RF Tx/Rx modules.

These modules, once a rare commodity, are now widely and

cheaply available. In this particular discussion, we shall be

using ASK (Amplitude Shift Keying) based TX/RX pair

operating at 433 MHz

Fig. 5: ASK Transmitter and Receiver.

The transmitter module accepts serial data at a

maximum of XX baud rate. They can be directly interfaced

to a microcontroller or can be used in remote control

applications with the help of encoder/decoder ICs. The

encoder IC takes in parallel data at the TX side packages it

into serial format and then transmits it with the help of a RF transmitter module. At the RX end, the decoder IC receives

the signal via the RF receiver module, decodes the serial data

and reproduces the original data in the parallel format. Now

in order to control say one motor, we require 2 bits of

information while we need 4 bits of information to control 2

motors. HT12E and HT12D is 4 channel encoder/decoder

ICs directly compatible with the specified RF module.

e) Wheel chair control:

Receiver receives the data in serial form then it decodes

that data and at last it is again converted into parallel form

and given to the receiver side CPU. At the receiver side the

decoder circuit IC HT 12D is used as a decoder. At the

decoder again the codes are received in serial form which

then again converted into parallel form. These decoded signals are then given as an input to CPU. At the receiver

side the IC MN4519 is used as the buffer.

-

CNTRL=0

R30

6K8

VCC

VCC +12V

U10A

NAND2

12 3

VREF

+12V

2K7

VREF

-

2K7

1 2

NOT

12

+

U13

NOT

12

+12V

VREF

NOT

12

+

-

U19C

LM339

9

814

312

PULSE

R31

2K7

TIP-127

TIP-122

+5V

2K7

DIR/1

VCC

TIP-127

PNP

DC--MOTOR

VCC

Q8

NPN

7404

VCC

+

-

U20B

LM339

5

42

312

2K7

+

-

U18A

LM339

7

61

312

+12V

+12V

U14

NOT

1 2

2K7

DC MOTOR CONTROL CARD

2K7

DIR/2

NOT

1 2

+

-

U21D

LM339

11

1013

312

Q11

PNP

7404

PAD4

OCPAD

Q9

NPN

12V

7404

7400

U11B

NAND1

12 3

2K7

2K7

7404

VREF

VREF

+

TIP-122

7400

DIR 1 DIR 2

CONTROL

-

+-

+

+

-

-

+

A B

NAND

NOTCOMP

NAND

NOT

COMP.

NOTCOMP.

1

2

3 adc

4

5

6

7

8

3

Fig. 6: Robot Control.

The nature of this buffer is FIFO that is First In First

Out. In order to drive motors, we would need to connect a

suitable motor driver at the output of the decoder IC. The

motor driver circuit can consist of a Relay, transistorized H-

Bridge or motor driver ICs like the L293D, L298 etc.

VIII. SOFTWARE DESIGN

Keyword recognition algorithm is designed according to

the block diagram as shown in figure below.

Fig. 7: Block diagram of Mel Frequency Cepstral Coefficient

Speech recognition algorithm is written in matlab 7.0 and

results are tested in clean or noisy test data. The explanation

and results are discussed in main program step by step as

shown below:

Step1. Declare variables:

clear all; % clear all variables

close all; % close all files

clc % clear screen ncoeff = 13; %Required number of mfcc coefficients

N = 8; %Number of words in vocabulary

k = 4; %Number of nearest neighbors to choose

fs=16000; %Sampling rate

duration1 = 0.15; %Initial silence duration in seconds

duration2 = 2; %Recording duration in seconds

G=2; %vary this factor to compensate for amplitude

variations

NSpeakers = 5; %Number of training speakers

Step2. Input Keyword and perform EPD:

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 19

Fig. 8: End Point Detection for Hindi Key word ―AAGE‖.

for i=1:8; % Check real time 8 keywords

fprintf('Press any key to start %g seconds of speech

recording...', duration2);

pause; % Wait for 0.15 second

silence = wavrecord(duration1*fs, fs); %Record keyword

fprintf('Recording speech...'); speechIn = wavrecord(duration2*fs, fs); % duration*fs is the

total number of sample points

Fig. 9: After End Point Detection for Hindi Key word

―AAGE‖.

Step3. Addition of silence:

p=length(speechIn)-length(silence);

for i=1:p

silence=[silence ;0]; end

fprintf('Finished recording.\n');

fprintf('System is trying to recognize what you have

spoken...\n');

speechIn1 = [silence;speechIn]; %pads with 150 ms

silence

speechIn2 = speechIn1.*G;

Fig. 10: Addition of silence 0.15 seconds in Hindi key word

―AAGE‖.

Step4. Noise Reduction:

speechIn3 = speechIn2 - mean(speechIn2); %DC offset

elimination

speechIn = nreduce(speechIn3,fs); %Applies spectral

subtraction

Fig. 11: After noise reduction for Hindi key word ―AAGE‖.

Step5. Windowing, DFT and Mel filter bank:

rMatrix1 = mfccf(13,speechIn,fs); %Compute test feature

vector

Fig. 12: Shows the time signal of the Hindi key word AAGE

and Mel filter bank of the word computed via FFT.

Step6. Inverse DFT:

rMatrix = CMN(rMatrix1); %Removes convolutional noise

Sco = DTWScores(rMatrix,N); %computes all DTW scores

[SortedScores,EIndex] = sort(Sco); %Sort scores increasing

K_Vector = EIndex(1:k); %Gets k lowest scores

Neighbors = zeros(1,k); %will hold k-N neighbors

Fig.13: DCT and Spectrogram for ‗AAGE‘ Key Word.

% Code below uses the index of the returned k lowest scores

to determine their classes

for t = 1:k

u = K_Vector(t);

for r = 1:NSpeakers-1

if u <= (N)

break

else u = u - (N);

end end

Neighbors(t) = u;

end

International Journal of Electrical & Electronics Engineering 20 www.ijeee-apm.com

Fig.14: Result for keyword recognition ‗AAGE‘ Key Word.

%Apply k-Nearest Neighbor rule Nbr = Neighbors[Modal,Freq] = mode(Nbr); %most frequent

value

Word = strvcat('Forward-AAGE', 'Reverse-PICHE', 'Break-

RUKO', 'Thirty-TEESH', 'Fifty-PACHAS', 'low-DHERE',

'Medium-TEJ', 'Stop-BAND');

if mean(abs(speechIn)) < 0.01

fprintf('No microphone connected or you have not said

anything.\n');

elseif ((k/(Freq)) > 2) %if no majority

fprintf('The word you have said could not be properly

recognised.\n');

else fprintf('You have just said %s.\n',Word(Modal,:)); %Prints

recognized word

end

IX. RESULT DISCUSSION

We made two experiments, in noise and in clean

environment one using traditional method (Md. Rashidul

Hasan et al. 2004) and the other using the developed

technique. The templets were used as input to the same

recognition system using DTW in order to measure the

performance for each method. First experiment uses the

traditional method (Md. Rashidul Hasan et al. 2004). The

dictionary contains Hindi key words and digits. For each

hindi key word and digits were selected a number of

templates from several training candidates (4-10) and second

experiment use 8 templates. A new generated template was

used for each key word and digit. Both experiments were

speaker dependent. The test was made using 8 test records

for each key words and digits. The accuracy for Hindi Key

Word recognition is calculated by speaking one command 10 times and find out how many times it recognize Key

Words with different rate of speech. Chart shows

approximately 91.25 % accuracy with end point detection

when user 1 say key Word in 10 × 12 room with noise

environment (Fan On, Tv On, and Cooking in Kitchen) and

without end point detection average accuracy is 80.00 %.

Figure shows chart for Hindi key word recognition in noise

environment with or without EPD.

Fig. 16: Shows results in chart for clean environment with or without EPD.

Chart shows approximately 97.50 % accuracy with end

point detection when user 1 say key Word in 10 × 12 room

with clean environment (Fan Off, Tv Off, No Cooking in

Kitchen) and without end point detection average accuracy is

87.50 %. Figure 2 shows chart for hindi key word

recognition in clean environment with or without EPD. After

calculating MFCC features, DTW finds nearest distance

between spoken word and recorded samples of 10 speakers.

If nearest distance of recorded samples matches with five or

more samples then it will show output and related to key word operation performed, if match is below five samples

then play recording word not recognized please try again.

X. CONCLUSION AND FUTURE WORK

This paper presents a simple technique for word

detection using end point detection, feature extraction using

Mel frequency cepstral coefficient and feature matching

using dynamic time warping. The implemented algorithm

and control system control fan speed, temperature of heater

and robot direction using the voice key word. It

demonstrates its reliability and ease of future development. Based on obtained experimental results it demonstrates that

the proposed algorithm is indeed functional and it can be

used in voice key word recognition home automation system

and industrial robots. Percentage of correct recognition of

key word is high enough. The recognition results are tested

for clean and noisy test data. The system can be said to be

robust as average accuracy for clean data is 97.50 while that

for noisy data is 91.25 %.

The main contribution of this study is that it presents the

idea of Hindi key word recognition and Home Automation

0102030405060708090

100

WITH EPD

(91.25%)

WITHOUT

EPD (80.00 %)

0

20

40

60

80

100

AA

GE

PIC

HE

RU

KO

DH

EE

RE

TE

J

TE

ES

PA

CH

AS

BA

ND

WITH EPD (97.50%)

WITHOUT EPD (87.50%)

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 21

system. The experiments also show that the approach is good for Hindi key word recognition. The proposed ASR and

Control System was completely implemented, our effort will

be directed toward developing the more appropriate and

convenient method.

REFERENCES [1] A. Rathinavelu, G.Anupriya, A.S.Muthanantha murugavel,

“Speech Recognition Model for Tamil Stops”, Proceedings of the World Congress on Engineering, ISBN:978-988-98671-5-7, Vol I, pp. 543 – 547, July 2 - 4, 2007.

[2] Adriana. Tapus and Brian Scassellati, “The grand challenges

in helping humans through social robotics”, IEEE Robotics & Automation Magazine, Vol 14, Issue 1, pp. 35–42, 2007.

[3] Anjli Bala, Abhijeet Kumar and Nidhika Birla, “Voice Command Recognition System Based on MFCC and DTW”, International Journal of Engineering Science and Technology, ISSN: 0975-5462, Vol. 2, No 12, pp. 7335-7342, Dec. 2010.

[4] Atanas Ouzounov (2010) “Acestral Feature and Text Dependent Speaker Identification-A Comparative stdy”, Cybernetics and Information Technologies, Vol. 10, No. 1, pp.

1-12, 2010. [5] B. H. Juang and Lawrence R. Rabiner, “Automatic Speech

Recognition – A Brief History of the Technology”, Vol. 10, No. 3, August 2004

[6] Bengt J. Borgstrom, “HMM-Based Reconstruction of Unreliable Spectrographic Data for Noise Robust Speech Recognition”, IEEE Transactions on Audio and Language Processing, Vol. 18, No. 6, pp. 1612-1623 August 2010.

[7] Bharti W. Gawali, Santosh Gaikwad, Pravin Yannawar, Suresh C.Mehrotra, “Marathi Isolated Word Recognition System using MFCC and DTW features”, ACEEE Int. J. on Information Technology, Vol. 01, No. 01, Mar 2011.

[8] Cini Kurian and Kannan Balakrishnan, “Automated Transcription System for Malayam Language”, International Journal of Computer Application, Vol. 19, No. 5, April 2011.

[9] F. K. Soong, A. E. Rosenberg, L. R. Rabiner and B. H. Juang,

“A Vector Quantization Approach to Speaker Recognition”, Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '85, vol 10, No 3, pp. 387-390, 1985.

[10] Fausto ―Tito‖ Poz and Durand R. Begault, “Voice Identification and Elimination Using Aural Spectographic Protocol”, AES 26th International Conference, Denver, Colorado, USA, 7–9 July 2005.

[11] Josef Rajnoha et al. (2011) “ASR systems in Noisy

Environment: Analysis and Solutions for Increasing Noise Robustness”, Radioengineering, Vol. 20, No. 1, April 2011.

[12] K. H. Davis, R. Biddulph and S. Balashek, ―Automatic Recognition of spoken digits”, The Journal of the acoustical society of america, vol 24, No 6, November, 1952.

[13] K. M. Ravikumar, R. Rajagopal and H. C. Nagaraj, “An Approach for Objective Assessment of Stuttered Speech Using MFCC Features”, DSP Journal, Volume 9, Issue 1, June, 2009.

[14] Khalid Saeed, “Sound and Voice Verification and Identification A Brief Review of Töeplitz Approach”, Znalosti 2008, pp. 22-27, 2008.

[15] Lindasalwa Muda, Mumtaj Begam and I. Elamvazuthi, “Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques”, Journal of Computing, ISSN 2151-9617, Volume 2, Issue 3, March 2010

[16] M. A. Anusuya and S. K. Katti, “Speech Recognition by Machine: A Review”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 6, No. 3, 2009.

[17] Maayan Geffet, Yair Wiseman and Dror Feitelson, “Automatic Alphabet Recognition”, Springer Science, Vol. 8, pp. 25–40,

2005. [18] Mark D. Skowronski and John G. Harris, “Improving the

Filter Bank of a Classic Speech Feature Extraction Algorithm”, IEEE Intl Symposium on Circuits and Systems, Bangkok, Thailand, vol 4, pp. 281-284, May 25 - 28, 2003.

AUTHORS

First Author– Abhishek Thakur:

M. Tech. in Electronics and

Communication Engineering from Punjab Technical University, MBA

in Information Technology from

Symbiosis Pune, M.H. Bachelor in

Engineering (B.E.- Electronics)

from Shivaji University Kolhapur,

M.H. Five years of work experience

in teaching and one year of work experience in industry.

Area of interest: Digital Image and Speech Processing,

Antenna Design and Wireless Communication. International

Publication: 7, National Conferences and Publication: 6,

Book Published: 4 (Microprocessor and Assembly Language Programming, Microprocessor and Microcontroller, Digital

Communication and Wireless Communication). Working

with Indo Global College of Engineering Abhipur, Mohali,

P.B. since 2011.

Email: abhithakur25@gmail.com

Second Author – Rajesh Kumar is

working as Associate Professor at

Indo Global College of Engineering,

Mohali, Punjab. He is pursuing

Ph.D from NIT, Hamirpur, H.P. and

has completed his M.Tech from GNE, Ludhiana, India. He

completed his B.Tech from HCTM,

Kaithal, India. He has 11 years of academic experience. He

has authored many research papers in reputed International

Journals, International and National conferences. His areas

of interest are VLSI, Microelectronics and Image & Speech

Processing.

Third Author – Amandeep Batth:

M. Tech. in Electronics and

Communication Engineering from Punjab Technical University,

MBA in Human Resource

Management from Punjab

Technical University , Bachelor in

Technology (B-Tech.) from

Punjab Technical University . Six

years of work experience in teaching. Area of interest:

Antenna Design and Wireless Communication. International

Publication: 1, National Conferences and Publication: 4.

Working with Indo Global College of Engineering

Abhipur, Mohali, P.B. since 2008. Email: amandeep_batth@rediffmail.com

Fourth Author – Jitender Sharma: M. Tech. in Electronics

and Communication Engineering from Mullana University,

Ambala, Bachelor in Technology (B-Tech.)from Punjab

Technical University . Five years of work experience in

teaching. Area of interest:, Antenna Design and Wireless

International Journal of Electrical & Electronics Engineering 22 www.ijeee-apm.com

Communication. International Publication: 1 National

Conferences and Publication:6 and Wireless

Communication). Working with Indo Global college since

2008.

E-mail: er_jitender2007@yahoo.co.in

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 23

IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426

CHAIN BASED WIRELESS SENSOR

NETWORK ROUTING USING HYBRID

OPIMIZATION (HBO AND ACO)

Er.Sadhna 1, Er.Supreet singh

2

1Electronics and Communication Deptt., Swami Parmananad College of Engineering & Tech., Punjab, India 2Electronics and Communication Deptt., Baba Banda Singh Bahadur College of Engineering, Punjab, India

ABSTRACT: In Wireless Sensor Network, due to the

energy restriction of each nodes, efficient routing is very

important in order to save the energy of the hybrid

optimization technique. The results of new protocol i.e.

hybrid have been compared with EEPB and IEEPB.

Simulation results show that the lifetime of Hybrid is better

as compared to EEPB and IEEPB. Throughput has been

increased in the Hybrid since in 50% node mobility EEPB give 1854, IEEPB give 1981 and HEEPB gives 2390 . Thus,

the proposed protocol is more energy efficient as compared

to chain based protocols i.e. EEPB and IEEPB sensor node

and to enhance the lifetime of the network. In this

dissertation, a new Optimization Tech.i.e. HYBRID(ACO

and HBO) with Improved PEGASIS protocol has been

designed.A new approach has been used to overcome the

problem of PEGASIS by using.

KEYWORDS: Wireless sensor network, Energy efficient

PIGASIS based,Improved Energy efficient PIGASIS

based,Hybrid Energy efficient PIGASIS based,Improved

I. WIRELESS SENSOR NETWORKS

A Wireless Sensor Network (WSN) consists of a large

number of tiny wireless sensor nodes (often referred to as

sensor nodes) that are, typically, densely deployed. Ad hoc

networks are defined as the category of wireless networks

that utilize multi-hop radio relaying since the nodes are

dynamically and arbitrarily located. Ad hoc networks are

infrastructure independent networks.

• Sensor Node: A sensor node is the core component of a

WSN. The sensor nodes can take on multiple roles in a

network, such as simple sensing; data storage; routing; and

data processing.

• Clusters: Clusters are the organizational unit for WSNs.

Because of the dense nature of these networks it requires the

need for them to be broken down into clusters to simplify

tasks such a communication [2].

• Cluster heads: Cluster heads are the organization leader of a cluster. They often are required to organize activity in the

cluster. These tasks are not limited to data-aggregation and

organizing the communication schedule of a cluster [3].

•

Base Station: The base station is at the upper level of the

hierarchical WSN. It provide the communication link

between the sensor network and the end-user.

• End User: The data in a sensor network can be used for a

wide-range of applications [1]. Therefore, a particular

application may make use of the network data over the

internet using a PDA or even a desktop computer

Fig1. Wireless Network

II. ENERGY EFFICIENCY IN WIRELESS SENSOR

NETWORKS A sensor network consists of a large number of small, low-

cost devices with sensing processing, and transmitting

capabilities. Main goal of the operation is to observe a region

and gather and relay information to a sink node or set of sink

nodes, called Base Station (BS). Forwarding the data to the

BS is possible in two ways: using direct or multihop

communication. In the first case every sensor transmits its

data directly to the sink; in the second case, the sensors are

communicating with the neighbours that forward the

information in the direction of the sink [3].

The sensors are usually deployed densely and often on-the-

fly. They operate un-tethered and unattended, are limited in power, computational capacities and memory. Because of

these constraints the sensor network must have efficient self-

organizing capabilities, while optimizing energy

consumption. A primary design issue in sensor networks is

energy efficiency. The main goal is to prolong the lifetime of

the network, which can be defined in several ways [4]:

• The time when the first node depletes its battery,

• The time until a given percentage of the sensors has

enough energy to operate,

• The time until a given percentage of the region is

covered by alive sensors.

International Journal of Electrical & Electronics Engineering 24 www.ijeee-apm.com

III. ROUTING PROTOCOLS IN WSN Energy consumption can be reduced by the use of various

techniques like data aggregation, clustering, data-centric

methods, etc. The routing protocols can be classified as flat,

hierarchical or location-based as follow:

Flat networks: In flat networks, all nodes are equal. Hence each node plays the same role. This network

has no logical hierarchy. It uses a flat addressing

scheme. Routing Information Protocol (RIP) is an

example of a flat routing protocol.

Hierarchical networks: In hierarchical networks,

the nodes are partitioned into a number of small

groups called clusters. Each cluster has a cluster

head (CH) which is the coordinator of other nodes.

These CHs perform data aggregation so that energy

inefficiency may be reduced. The cluster heads may

change. The node which has the highest energy acts as the CH. Hierarchical routing is an efficient way

to lower energy consumption within a cluster. It has

major advantages of scalability, energy efficiency,

efficient bandwidth utilization, reduces channel

contention and packet collisions. Low Power

Adaptive Clustering Hierarchy (LEACH), Power

efficient gathering in sensor information and

(PEGASIS), Hybrid Energy-Efficient Distributed

Clustering (HEED), etc. are examples of

hierarchical networks.

PEGASIS

Hierarchical-based routing protocols are widely used for their high energy-efficiency and good expandability. The idea of

them is to select some nodes in charge of a certain region

routing. These chosen nodes have greater responsibility

relative to other nodes which leads to the incompletely equal

relationship between sensor nodes. It is the typical

hierarchical-based routing protocols. As an enhancement

algorithm of PEGASIS is a classical chain-based routing

protocol. chain based protocol saves significant energy

compared with the LEACH protocol by improving the cluster

configuration and the delivery method of sensing data.

However, the PEGASIS protocol also has many problems requiring solutions. In recent years, researchers have

proposed many improved algorithms based on PEGASIS

such as PEG-Ant, PDCH and EEPB et al.

•When EEPB builds a chain, the threshold adopted is

uncertain and complex to determine, which causes the

inevitability of LL if valued inappropriately.

• When EEPB selects the leader, it ignores the suitable

proportion of nodes energy and distance between node and

BS which optimizes the leader selection according to various

application environments. Based on the above analysis, this

paper presents an improved energy-efficient PEGASIS-based

routing protocol called IEEPB. IEEPB compares the distance between nodes twice, finds the shortest path to link the two

adjacent nodes. This chain-building method is more

simplified and effectively avoids the formation of LL

between neighbouring nodes.

IV.WSN USING HYBRID HBO AND ANT

OPTIMIZATION TECHNIQUE

A Wireless Sensor Network (WSN) consists of a large

number of tiny wireless sensor nodes (often referred to as

sensor nodes or, simply, nodes) that are, typically, densely

deployed. Energy efficiency is the most required quality in a sensor network where each node consumes some energy with

each transmission over the network. Energy efficiency is

required to improve the network life. Our proposed work is

defined to improve the energy efficiency in Wireless Sensor

Networks. The two algorithms from Artificial intelligence

will be used in our work. Also the PEGASIS protocol will

be enhanced and then implemented in the WSN scenario. In our work, we will take following parameters into

consideration:

I. Average energy per iteration

II. No of alive nodes per iteration

V. SIMULATION ENVIRONMENT

A 100 node field is used and generated by randomly placing

the nodes in a 100 m x 100 m square area. We assume that

the area contains homogeneous sensor nodes with a

communication range of 45m. The simulation focuses on

number of sensor nodes alive, Average Energy of network

and cost slot per iterations which are important indicators to measure performance of different algorithms. The simulation

parameters used are shown below:

Table 1: Simulation Parameters

Parameters Values

Number of Nodes 100

Area Size 100×100

Base Station (50, 300)

Energy Transmitted 50nj/bit

Energy Received 100pj/bit/m2

Amp Energy 0.0013pj/bit/m4

V1.SIMULATION RESULTS

0 500 1000 1500 2000 2500 30000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Number of rounds

Avera

ge E

nerg

y p

er

round

IEEPB

HEEPB

EEPB

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 25

0 500 1000 1500 2000 2500 30000

10

20

30

40

50

60

70

80

90

100

Number of rounds

Num

ber

of

alive n

odes p

er

round

IEEPB

HEEPB

EEPB

Table 2 Network life time

VII.CONCLUSION & FUTURE SCOPE

A new enhanced scheme based on artificial intelligence has

been proposed for Wireless Sensor Networks which helps to

improve the energy efficiency as well as lifetime of the

Wireless sensor network. Energy efficiency is the most

required quality in a sensor network where each node consumes some energy with each transmission over the

network. Energy efficiency is also required to improve the

network life. The results of the proposed scheme are

evaluated in MATLAB.The simulation results shows that the

proposed scheme that is hybrid Honey bee optimization and

ant colony optimization with improved PEGASIS has the

better results as compare to previous techniques. In this

proposed work chain complexity is reduced by using hybrid

optimization technique and is more efficient in energy

saving.

In future, the work can be extended by reducing the complexity of chain further by optimizing the energy

parameter along with the distance parameter or the nutrient

function can be changed.

REFERENCES

[1] Z. M Wang, S.Basagni, E.Melachrinoudis and C.Petrioli, ‗‗Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime‘‘, Proceedings of the 38th Hawaii International Conference on System Sciences, IEEE Computer Society, 2005. [2] E. H. Callaway, Wireless Sensor Networks, Architectures and

Protocols, Auerbach Publications, Taylor & Francis Group, Boca Raton, Fla, USA, 2003. [3] Thanos Stathopoulos, R. Kapur, D.Estrin, ―Application-Based Collision Avoidance in Wireless Sensor Networks‖, Conference of Computer society, July-December 2005. [4] K. Padmanabhan, Dr. P. Kamalakkannan,― Energy-efficient Dynamic Clustering Protocol for Wireless Sensor Networks‖, International Journal of Computer Applications, Vol. 38, Issue. 11,

January 2012. [5] S. R. Das, C. E. Perkins, and E. M. Royer, ―Ad hoc on-demand distance vector (AODV) routing‖, IETF Internet draft, draft-ietf-manet-aodv- 13.txt, Feb 2003. [6] S.K Singh, M. P Singh and D K Singh , ―Routing Protocols in Wireless Sensor Networks –A Survey,‖ International Journal of Computer Science & Engineering Survey (IJCSES) Vol.1, No.2, November 2010. [7] P.Tyagi, R.P Gupta, R.K Gill,‖ Comparative Analysis of

Cluster Based Routing Protocols used in Heterogeneous Wireless Sensor Network‖, International Journal of Soft Computing and Engineering (IJSCE), Vol. 1, Issue. 5, November 2011.

Node

mortality

EEPB

IEEPB

HEEPB

1%

387

993

2100

50%

1854 1981 2390

100%

1902 2047 2420

International Journal of Electrical & Electronics Engineering 26 www.ijeee-apm.com

IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426

Technical Recapitulation on LI-FI 1Maninder Singh,

2Dr.Hardeep Singh Saini,

3Dr. Pooja Sahni

1,2,3Indo Global college of Engineering, Punjab, India 1md_singh1989@yahoo.com,

2hardeep_saini17@yahoo.co.in,

3sehgalpuja2004@gmail.com

Abstract— Li-Fi stand for ―light fidelity‖, it is a wireless optical networking technology that uses light-

emitting diodes (LEDs) for data transmission. Li-Fi is

different from Wi-Fi that transmits data by using the

spectrum of visible light. As with increasing in demand for

wireless application and data rates associated with it we

explain the use of Li-Fi as a wireless technology for large

deployments, in the paper we present the introduction to

Li-Fi ,its history, architecture, features of Li-Fi and at the

last a conclusion is concluded.

1. INTRODUCTION

Li-Fi stands for ―light fidelity‖. Like Wi-Fi it is method of

transmitting data form one section to another wirelessly.

But in the case of Wi-Fi uses Radio Waves for

transmitting the data and in the Li-Fi uses Light to

communicate data/transmitting data. It is 5G[1] visible

light communication systems technology which using light

from light-emitting diodes (LEDs) as a medium to

transport networked, mobile and the high-speed

communication. All these function same as Wi-Fi and Optical fiber [2]. It leads to the Internet of things, in

which every electronic devices are connected with the

internet and the LED lights which used as an internet

access point [3].

Today in the market of Li-Fi, its annual growth rate is

near about 82% from 2013 to 2018 and to be worth over

$6 billion per year by 2018[4]. VLC, which means

―Visible light communications‖, VLC signals work by

switching the bulbs on and off within nanoseconds [5]. In

Li-Fi bulbs are kept on to transmit end in which it transmit

the data, the bulbs could be dim to the purpose that they weren't visible to the humans and but still purposeful [6].

The light wave‘s which cannot go through(penetrate) the

walls which makes a much shorter range, so this features

make it more secure from hacking, relative to the Wi-

Fi[7][8]. In the Li-Fi to transmit the signal line of sight is

not necessary and light reflected off of the walls can attain

70 Mbps [9]. The advantage of Li-Fi is that the actinic ray

is much a lot of plentiful than the spectrum (10,000 times

a lot of in fact) and may attain so much larger information

density (fig1).

Fig1. Spectrum radio versus light [20]

2. HISTORY

The University of Edinburgh in the UK, Professor Harald

Haas, is the original founder of Li-Fi [10]. Li-Fi is a VLC

(visible light communication) which includes use of the

visible light section of the electromagnetic spectrum to

transmit the information of the signals. At Edinburgh's

Institute, the D-Light project for Digital Communications

was funded from Jan 2010 to Jan 2012[11].

Professor Harald Haas, promoted this technology in his

2011 TED Global talk [12]. Pure Li-Fi, formerly pure

VLC, it's basically a creative instrumentality manufacturer

(OEM) firm discovered to commercialize Li-Fi

merchandise for integration with conferred LED-lighting

systems [13][14]. In the Consumer Electronics Show in

Las Vegas from January 7–10 in 2014, the first Li-Fi Smartphone prototype was presented. The phone uses

Sun Partner‘s Wysips CONNECT. it's a way that converts

light waves into useful energy and creating the phone

capable of receiving and cryptography the signals simply

while not drawing on its battery [15][16].

In this a flimsy layer of precious stone glass might be

added to little screens like watches and cell phones that

make them sun based fueled. Cell phones could build 15%

more battery life throughout a regular day. This first cell

phones utilizing this kind of engineering ought to show up in 2015. This screen can additionally work to acknowledge

Li-Fi signs thus can the cell phone Polaroid [17] .The sort

screens cost for every cell phone is between $2 and $3,

which is much less expensive than most new engineering

[18]. What's more this sort innovation is introduced in

display centers and organizations crosswise over France,

and is continuously grasped by EDF, which is one of the

country's biggest utilities [17]

For shoppers at stores, the Philips lighting company has

developed a Li-Fi system. In this they can easily download

an app on their smart phone and then their smart phone works with the LEDs in the store. It can pinpoint where

they are at in the store and give them corresponding

coupons and information based on where aisle they are on

and what they are looking at [19].

3. WORKING OF LI-FI TECHNOLOGY

The working of Li-Fi engineering is basic. Form the fig2

we seen that a light source toward one side like a LED and

a photograph identifier (Light Sensor) on the flip side.

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 27

Fig2. Working of Li-Fi Technology [20]

The point when LED begins gleaming, photograph finder or light sensor on flip side will discover light and get a

paired 1 generally double (otherwise) 0. How can

information be transmit by means of this new Li-F-

engineering? Blazing a LED sure times will advance a

message to transmit. Blazing of light is located by the

photograph indicator or light sensor and it will gain a

message.[20]

When a relentless current is applied to Associate in

Nursing LED light-weight bulb a relentless stream of

photons area unit emitted from the bulb that is discovered

as actinic radiation. If this is varied slowly the output intensity of the sunshine dims up and down. As a result of

LED bulbs area unit semi-conductor devices, this, and

therefore the optical output, is modulated at

extraordinarily high speeds which might be detected by a

photo-detector device and reborn back to electrical

current. The intensity modulation is indiscernible to the

human eye, and so communication is simply as seamless

ad RF. mistreatment this system, high speed info is

transmitted from Associate in Nursing LED light-weight

bulb.Radio frequency communication needs radio circuits,

antennas and sophisticated receivers, whereas Li-Fi is far easier and uses direct modulation ways kind of like those

utilized in inexpensive infra-red communications devices

like device units. Infra-red communication is restricted in

power as a result of eye safety needs, whereas LED light-

weight bulbs have high intensities and may come through

terribly massive information rates [20].

Fig3. Show how Li-Fi transmit and receive signal [20]

Now, think about many LEDs with some totally different

colours, flashing along and building a large info to

transmit. it's ascertained that inexperienced optical maser with the red optical maser will transmit knowledge at one

GBPS.

Binary information is made up of strings of 1‘s and 0‘s.

Any light source can transmit this ON and OFF

information but LEDs are capable of height flickering

speed. Light receivers interpret the flickering LED as 1‘s

and 0‘s and thus we have our Li-Fi, light off=0 and light

on=1. Why is Li-Fi so much faster? Because visible light

is far more dense than radio waves 10,000 times (fig1)

more dense in fact, meaning much more data can be

transferred. What is so special about Li-Fi? The speed, the highest speed yet recorded with a Li-Fi connection is

10Gbt/s which is 250 times faster than the average

broadband speed [21],[22] .This estimate is with high –end

instrumentality, but industrial Li-Fi being created in china

is at concerning a hundred and fifty Mbps, that continues

to be ten times above the typical United Kingdom of Great

Britain and Northern Ireland affiliation speed. Some

specialists claim that Li-Fi represents the longer term of

mobile net, its reduced prices and larger potency compared

with Wi-Fi [4][5].

Wi-Fi and Li-Fi each transmit information over the

spectrum, however whereas Wi-Fi uses radio waves and

whereas Li-Fi uses visible radiation. This is often a

advantage therein the visible radiation is way additional

plentiful than the spectrum (10,000 times additional in

fact) and may attain way larger information density [7][8].

A venture between the universities of Strathclyde,

Edinburgh, Cambridge‘s Andrews and, Oxford during this

analysis was administered by the Ultra Parallel visible

radiation Communications project and funded by the

Engineering and Physical Sciences analysis Council

[23].The existing light-emitting diode lightweight bulbs can be reborn to transmit Li-Fi signals with one

semiconductor device, and therefore the technology would

even be of use in things wherever radio frequencies can't

International Journal of Electrical & Electronics Engineering 28 www.ijeee-apm.com

be used for concern of meddling with electronic

equipment.

4. ADVANTAGES

1. Li-Fi has higher speeds than Wi-Fi [4] [5]. 2. It has 10000 times the frequency spectrum of

radio [23].

3. Li-Fi safer than Wi-Fi with hackers unable to access unsecured net connections from out of

sight of the transmitter [9].

4. Because Li-Fi does not use radio waves as Wi-Fi

does. It does not interface with radio

communication; this means Li-Fi can be used

safely during flights [7] [8].

5. Although Wi-Fi can penetrate walls, this is not

always desirable, Li-Fi can prevent internet

piggybacking and may offer a more secures

connection for those in for example intelligence

agencies r embassies [7] [8].

6. For project that deal with massive amount of data such as at CERN with large Hadron collider. Li-

Fi s the clear winner over Wi-Fi.and wired

connections.

5. CONCLUSION

Li-Fi technology might change larger space of coverage

than one Wi-Fi router thanks to all the lights in and around

a building. The Drawbacks to the technology embrace the

necessity for a transparent line of sight, difficulties with

quality and therefore the demand that lights continue for

operation. The probabilities area unit varied and may be explored additional. If his technology may be place into

sensible use, each bulb may be used one thing sort of a

Wi-Fi hotspot to transmit wireless knowledge and that we

can proceed toward the cleaner, greener, safer and brighter

future. The thought of Li-Fi is presently attracting a good

deal of interest, not least as a result of it's going to provide

a real and really economical various to radio-based

wireless. As a growing variety of individuals and their

several devices access wireless net, the airwaves have

become progressively clogged, creating it a lot of and

tougher to induce a reliable, high-speed signal. This could

solve problems like the shortage of radio-frequency information measure and additionally enable net wherever

ancient radio based mostly wireless isn‘t allowed like craft

or hospitals. One amongst the shortcomings but is that it

solely add direct line of sight.

REFERENCES

[1] The University of Edinburgh and National Instruments

Collaborate on Massive MIMO Visible Light Communication Networks to Advance 5G, Cambridge Wireless, 20 November 2013. [2]Light bulbs could replace your Wi-Fi router, Digital Trends, 30 October 2013, Joshua Sherman.

[3]Tech firm sees the light with £3m funding, The Scotsman, Peter Ranscombe, 24 December 2013. [4]Visible Light Communication (VLC)/Li-Fi Technology Market worth $6,138.02 Million - 2018, New International, 13 November 2013. [5] LiFi beats Wi-Fi with 1GB wireless speeds over pulsing LEDs, gearburn, 13 January 2013, Jacques Coetzee. [6]Condliffe, Jamie (28 July 2011). "Will Li-Fi be the new Wi-

Fi?". New Scientist.

[7] Li-Fi – Internet at the Speed of Light, by Ian Lim, the gadgeteer, dated 29 August 2011. [8] "Visible-light communication: Tripping the light fantastic: A fast and cheap optical version of Wi-Fi is coming". The Economist. 28 January 2012. Retrieved 22 October 2013.

[9] The internet on beams of LED light, The Science Show, 7 December 2013. [10] The Future‘s Bright, The Future‘s Li-Fi, Calendonian Mercury, 29 November 2013. [11] Povey,, Gordon. "About Visible Light Communications". pureVLC. Archived from the original on 18 August 2013. Retrieved 22 October 2013. [12] Haas, Harald (July 2011). "Wireless data from every light

bulb". TED Global. Edinburgh,Scotland. [13] "pureLiFi Ltd". pureLiFi. Retrieved 22 December 2013. [14] "pureVLC Ltd". Enterprise showcase. University of Edinburgh. Retrieved 22 October 2013. [15] Breton, Johann (20 December 2013). "Li-Fi Smartphone to be Presented at CES 2014". Digital Versus. Retrieved January 16, 2014. [16] Rigg, Jamie (January 11, 2014). "Smartphone concept

incorporates LiFi sensor for receiving light-based data". Engadget. Retrieved January 16, 2014. [17] An Internet of Light: Going Online with LEDs and the First Li-Fi Smartphone, Motherboard Beta, Brian Merchant. [18] Your next phone may charge and receive data through this incredible screen, Digital Trends, 19 January 2014, Jeffrey Van Camp. [19] Philips Creates Shopping Assistant with LEDs and Smart

Phone, IEEE Spectrum, 18 February 2014, Martin LaMonica. [20] http://en.wikipedia.org/wiki/Li-Fi. 21]Li-Fi revolution-: internet connections using light bulbs are 250 times faster than broadband, The Independent, James Vincent, 28 October 2013. [22] 'Li-fi' via LED light bulb data speed breakthrough, BBC News, Matthew Wall, 28 October 2013. [23]High-speed wireless networking using visible light, Spie, Harald Haas, 19 April 2013.

AUTHORS

Maninder Singh is following M.Tech

from Indo Global College of Engineering, India. He has completed B.Tech from

IGCE, Mohali (Punjab), India in the year

2011. He has two year of educational

expertise. Working as Assistant Professor

(ECE) at indo global college of Engineering, Abhipur

(Mohali) since June-2012.His areas of interest are wireless

and mobile communication, Optical communication.

Hardeep Singh Saini obtained his

Doctorate degree in Electronics and

Communication Engineering in 2012. He holds Master‘s degree in Electronic

and communication from Punjab

technical university, jalandhar passed

in 2007. His total experience is 15

year, presently, working as Professor (ECE) and Associate

Dean Academic at Indo Global college of Engineering,

Abhipur (Mohali), PUNJAB (INDIA) since June-2007. He

is author of 5 books in the field of communication

Engineering. He has presented 21 papers in international

/national conferences and published 30 papers in

international journals. He is a fellow and senior member of

various prestigious societies like IETE (India), IEEE, UACEE, IACSIT and he is also editorial member of

various international journals.

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 29

IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426

Capsulization of Existing Space Time Techniques

1Maninder singh,

2Dr.Hardeep singh Saini

Indo Global College of Engineering, Punjab, India 1md_singh1989@yahoo.com,

2hardeep_saini17@yahoo.co.in

Abstract— In this paper, we explore the fundamental concepts behind the emerging field of space-time coding for

wireless communication system. A space–time code (STC)

is a method which employed to increase the reliability of

data transmission in the wireless communication

systems using multiple transmit antennas. Space–time

code (STC) depends on transmitting

multiple, redundant copies of a data stream to the receiver in

the hope that at least some of them may live the physical

path between transmission and reception section with

reliable decoding.

Keywords— STC; STTC; BLAST;

1. INTRODUCTION

With the increase in demand of increasingly sophisticated

communication services available any-time, anywhere,

wireless communications has emerged as one of the largest

and most rapid and steadfastness sectors of the global

telecommunications industry. A quick look at the status quo

reveals that second and third generation cellular systems supporting data rates of 9.6 Kbps to 2 Mbps uses by a 700

million people around the world subscribe to existing. More

recently, in wireless LAN networks IEEE 802.11, which

provided 11 Mbps rate and attracted more than $1.6 billion

(USD) in equipment sales [1]. The capabilities of both of

these technologies over the next ten years, are expected to

move toward the 100 Mbps – 1 Gbps range [2] and

subscriber numbers to over 2 billion [3]. One of the most

significant technological developments of the last decade,

that promises to play a key role in realizing this tremendous

growth, is wireless communication using MIMO antenna

architectures.

A space time code (STC) which is used in the wireless

communication to improve the reliability of data

transmission. Space Time Code depends on transmitting

multiple, redundant copies of a data beam to the receiver.

The receiver which in the hope that at least one of them may

live the physical path between both transmission and

reception section. Space time code may be further divided

according to coherent STC and non coherent STC. When the

receiver section the channel impairment through training

called coherent STC[4] and in the non coherent is totally opposite to the coherent STC .Coherent STC basically is

used widely and division algebras over for making or

constructing codes[6,5],fig1.1 Space time code diagram.[7]

Fig 1.1Space time code diagram [7]

Space time techniques divide into two main parts (see in the

fig) -:

1) Transmit diversity

2) Spatial multiplexing

Fig 1.2 Classification of space time technique [3]

2.SPACE TIME TECHNIQUES

2.1Transmit diversity

2.1.1 Space time block codes –: The term Space-Time Code (STC) originally got into

existence in 1998 by Tarokh et al. to describe a new two-

dimensional way of encoding and decoding signals

transmitted over wireless fading channels using multiple transmit antennas.

In this technique, data stream of a multiple copies are

transmits across the number of antennas (MIMO) and this

technique improves the reliability of data transfer. Also, the

transmitted signal must transverse a potentially difficult

environment with scattering, reflection, refraction and then it

effects by thermal noise which effects the information or

International Journal of Electrical & Electronics Engineering 30 www.ijeee-apm.com

data in the receiver section. So, space- time coding basically

add all the copies of the received signal, so in this way it can

easily get the information.

It is divided into three sections. First one is flat quasi-static

fading channel is used in communication system operating

under narrow band conditions, second is frequency selective fading channel which is used in wideband communication

system[8].

2.1.1.1 Flat quasi-static channel-:

This is further divided into the first one is the Alamouti code

and second is extended version of Alamouti work on which

accommodates large number of transmit antennas, proposed

by Tarokh et al under the name of orthogonal designs. Lately

is linear depression code of Hassibi et al, which address the

capacity limitation of both of these codes and also support

arbitrary number of transmit antenna.

(a)Almouti Block Code-:

It is introduced to improve link-level performance based on

diversity. It is proposed a simple scheme for a 2*2 matrix

system that achieves a full diversity gain with a simple

maximum likelihood decoding algorithm. It is designed from

the view of diversity gain to increased the multiple antenna

transmission scheme in order to achieve the good

performance. Let in the case where these two transmit

antenna by arranging the input symbols (𝑥1,𝑥2) and input

their complex conjugates in a special 2*2 matrix.

𝑆 = 𝑥1 −𝑥2

∗

𝑥2 𝑥1∗

Each column of 𝑆 contains the symbols transmitted from the pair of antennas during a particular symbol period. We see

that second column is a permutation and a reflection of the

complex conjugate of the first. Then 𝑆 over flat fading channel, written as: where P is the appropriate permutation

reflection matrix.

ℎ−𝑇𝑆 = [ℎ−𝑇 x ℎ−𝑇P𝑥∗ ]

[(ℎ−𝑇𝑆 )1 (ℎ−𝑇𝑆 )2

∗] = ℎ−𝑇 (ℎ−𝑇P)∗]x

The principle of space time block coding with 2 transmit

antenna and one receive antenna is explained in the post

on Alamouti STBC. With two receive antenna‘s the

system can be modeled as shown in the figure below (fig2.1).

Fig2.1: Transmit 2 Receive Alamouti STBC

The Alamouti space-time block coding is a simple MIMO

technique which can be used to reduce the BER of a

system with a specific SNR and without any loss on the

data rate/information. The presented decoding technique is

called hard decision-based zero forcing and it is easily to

implement in hardware slot. [9]

(b)STBC based orthogonal design-:

It is basically advanced version of Almouti`s work. It

removes the capacity limitations. It also provides full

diversity gain. Example: the code N=U, transmit antenna is

given by

𝑆 =

𝑥1 −𝑥2−𝑥3 −𝑥4

𝑥2 𝑥1𝑥4 −𝑥3

𝑥3

𝑥4

−𝑥4

𝑥3

𝑥2 𝑥2

−𝑥2 𝑥1

We seen that each column of S differ from the first by

permutation reflection. Next, we consider a generalized real

orthogonal design, for N=3 transmit antenna.

𝑆 = 𝑥1 −𝑥2

−𝑥3 −𝑥4

𝑥2 𝑥1𝑥4 −𝑥3

𝑥3 −𝑥4𝑥1 𝑥2

It views like a counter intuitive at first complex orthogonal

design only exist for N=2 , namely the Almounti`s STBC .

Therefore generalized complex orthogonal design is derived

and various codes are constructed. So, generalized design for

N=4 is given by

𝑆 =

𝑥1 −𝑥2

𝑥2 𝑥1

−𝑥3 −𝑥4 𝑥1∗ −𝑥2

∗ −𝑥3∗ −𝑥4

∗

𝑥4 −𝑥3 𝑥2∗ 𝑥1

∗ 𝑥4∗ −𝑥3

∗

𝑥3 −𝑥4

𝑥4 𝑥3

𝑥2 𝑥2 𝑥3∗ −𝑥4

∗ −𝑥2∗ 𝑥2

∗

−𝑥2 𝑥1 𝑥4∗ 𝑥3

∗ −𝑥2∗ 𝑥1

∗

L=8 symbol periods are required to transmit Q=4 symbols, resulting in a significantly reduced rate but increased the

capacity offered by competitive MIMO scheme such as

BLAST [10,11]. STBC based on amicable designs, which

provide higher rates than those based orthogonal design for

some numbers of transmit and receive antennas [12] and

quasi-orthogonal STBC, which sacrifice diversity to achieve

rate 1 for some condition with more than two transmit

antennas.[13]

(c)Linear dispersion code-:

This is used to realize rates higher than 1 sym\s\hz\ using STBC transmission, Hassibi et.al. Study the effective

capacity of code based on orthogonal design. It basically

develops a new class of block code designed to maximize

the mutual information between the transmitted and received

signals. The resulting designs are called linear dispersion

codes. Codes for using a set of 2Q dispersion matrices

𝑆 = (𝑥𝑅𝑞𝑄𝑞=1 𝐴𝑞 + j𝑥𝐼𝑞𝐵𝑞 )

(1)

Where R stand for real part of complex valued structure and

it is imaginary part. For instance if Q=2 and

𝐴1 = 1 00 1

, 𝐵1 = 1 00 −1

, 𝐴2 = 0 −11 0

, 𝐵2 = 0 11 0

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 31

then the linear combination of (1) gives

𝑆 = 𝑥𝑅1 + 𝑗𝑥𝐼1 −𝑥𝑅2 + 𝑗𝑥𝐼2

𝑥𝑅2 + 𝑗𝑥𝐼2 −𝑥𝑅1 − 𝑗𝑥𝐼1

= 𝑥1 −𝑥2

∗

𝑥2 𝑥1∗

The limitation of LDC is that good designs are not known to

follow systematic or algebraic rules.[14]

2.1.1.2 Frequency Selective Fading Channel:

It is used in STBC for transmission over frequency selective

or multipath fading channel. In this there are two main parts,

in the first class are those techniques for single-carrier

modulation techniques systems that focus on reducing

equalization complexity and this techniques known as time –

reversal approach by LindsKog et al. that takes benefit of space- time code structure to decrease the dimensionality of

the equalization step.

The second classes of techniques are built around block

processing operations that effectively convert the frequency

selective channel into a set of flat fading sub-channels.

These may employ OFDM with multi-carrier modulation or

Frequency Domain Equalization with single-carrier

modulation.

(a)Time Reversal (TR) STBC-:

This technique is used for single-carrier modulation system

which focuses on reducing equalization complexity. The proposal in this area is a time-reversal approach by

Zindskog.et.al that takes advantage of the space time code

structure to decrease the dimension of the equalization step.

It is flat fading channel based on orthogonal design. They

are designed for use with single-carrier modulation in

which it simplifying the equalization by decoupling the

problem from LN dimension to N L-dimensional tasks

which may be executed in parallel. The TR-STRC involves

protecting data symbol columns by enclosing each of them

between guard columns of known symbols.

They will refer to these guard blocks as the prefix and suffix, both must be of length at least K - 1, and denote by the net

length of the protected data block. It is clear that there is

some rate loss associated with the guard blocks, which can

be reduced by increasing the size of the data block.

However, the maximum size of the data blocks is also

limited by the coherence time of the channel 2 In addition

𝐿 , data columns where complex conjugation is applied in the underlying code are transmitted in time-reversed order,

hence the name given to the code. The accompanying guard

blocks are also conjugated and time-reversed. The

transmitted signal matrix has the following general structure:

It have seen that channel be slowly fading so that

𝐿 = 𝐿0[𝐿 + 2(K-1)] symbol periods, whereas before 𝐿 denotes the gross block length including guard symbols and

𝐿0 is the block length of the underlying STBC design for flat

fading.

The main limitation of the TR-STBC is its limited rate

compared to the potential multiplexing gain available in the

MIMO channel. [15,16]

(b)STBC with frequency domain processing-: A number of researchers have also considered extensions of

the Alamouti scheme to systems using frequency domain

processing. One of the first proposals for combining STBC

with OFDM and multi-carrier modulation was put forward

by Mudulodu et al. Subsequently, two works based on

single-carrier transmission systems with frequency domain

processing at the receiver were presented by Al-Dhahir and

Zhou et al. All three approaches share substantially similar

signal matrix structures and thus we will follow [17] here.

In this work STBC over frequency selective fading channels

is proposed in combination with FDE. As we shall see, it

exhibits a structure that bears some resemblance to time-reversal, and thus shares many properties of the TR-STBC.

The transmitted signal matrix is of the form

We note that the rate achieved by this transmission scheme

is fractionally higher than that of the TR-STBC because it

does not require a guard suffix block. [19, 18]

2.1.2 Space time trellis codes (STTC)-:

It is used in the multiple antenna wireless communication. It

transmits multiple redundant copies of a convolutional code

or trellis code distributed over time and with a number of

antennas (MIMO). Then receivers use these multiple,

'diverse' copies of the data to reconstruct the actual

transmitted data. In space time block code, they are able to

International Journal of Electrical & Electronics Engineering 32 www.ijeee-apm.com

provide both coding gain and a better bit error rate

performance. But in space time trellis code they are more

complex than STBCs to encode and decode. They depend on

a viterbi decoder at the receiver where STBCs need only

linear processing. STTC were proposed by Vahid Tarokh et

al. in 1998. Just as trellis codes impose structure within each code word (cover the code space) and also between code

words transmitted in sequence (over time) the diversity gain

of STTCs is determined via a PEP argument. The PEP

expresses the probability of transmitting 𝑆𝑐 and deciding in

favour of 𝑆𝜀 at the decoder. Defining the code word

difference matrix

B = 𝑆𝑐 − 𝑆𝜀 with SVD B = U 𝑉+ and r = rank B

P(𝑆𝑐 → 𝑆𝜀

) < 𝜋𝑖=1 𝑀 𝜋𝑗 =1

𝑟 (𝜎𝑗2 𝑝

4)−1

=(det[𝐵𝐵+])−𝑀(𝑝

4)−𝑀𝑟 (2)

above equation(2) is coding gain of approximately,

𝛾 = [det(𝐵𝐵+)]1

𝑟 is achieved.

Fig 2.2: Space time trellis codes

It has high complexity so this is it main limitation. [20]

Comparison between STBC and STTC-:

STBC STTC

1. It has no coding gain. 1. It has coding gain.

2. Easily decodable by

maximum likelihood

decoding via linear

processing.

2. Conserve capacity

irrespective of the number of

antennas.

3. STBC is simple to design

based on orthogonal

sequences.

3. STTC is difficult to

design.

4.For one receive antenna

and state code, performance

is similar to STTC

4. STTC outperforms with

increasing antennas and

trellis states.

5. Easily lends itself to

industrial applications

because of its simplicity.

5. Complex to organize.

6. Loses capacity with two or

more receive antennas.

6.Conserve capacity

irrespective of the number of

antennas.

2.2 Spatial multiplexing –:

In view of the narrowband nature of the transmission, each

data stream follows only one route to the receiver and there

are no multipath experienced by the individual data streams.

In SM system, the maximum number of modulation symbols

that can be transmitted per symbol, maximum (𝑟𝑠) is given

by

max(𝑟𝑠)) =𝑁𝑡

which implies that the maximum spectral efficiency of an

SM system given by

𝜂𝑚𝑎𝑥 =𝑁𝑡𝑟𝑡 𝑙𝑜𝑔2(M)bps/Hz

Where 𝑟𝑡 s the rate of any conventional coding used in the

spatial multiplexing system and M is the modulation order.In

general, spatial multiplexing is achieved using a concept called layered space-time (LST) coding.[21]

2.2.1 Layered space time (LST)-:

Spatial multiplexing is achieved by raising a concept of

layered space time (LST) coding. Foschini proposed LST

architecture. In LST method, SM can also be achieved using

Eigen beam forming, it is a practical SM technique that is

used in most modern wireless communication system. They

are three main approaches are-:

Bell Laboratory layered space-time (BLAST) family of

techniques-:

a) V-Blast (Vertical-Blast)

b) H- Blast (Horizontal Blast)

c) D-Blast (Diagonal Blast

The type of decoding algorithm that is used is an important

consideration for LST coded SM system. Four decoding

schemes are-:

1) Zero Forcing (ZF)

2) Zero Forcing with interference cancellation (ZF-IC)

3) Linear minimum mean square error estimation

(LMMSE)

4) LMMSE with interference cancellation (LMMSE-IC)

(a) VERTICAL BLAST-:

In V-Blast the information bit stream is processed by an

optional conventional error encoder and then split into 𝑁𝑟

data stream, each of which is separately modulation before

being passed to its respective antenna for transmission. The

use of the adjective vertical in v-blast is a reference to the

fact that the input is split into parallel streams that are

depicted vertically in most diagrams encoder employs its

own modulator the V-blast architecture is capable of

accommodating applications where different data rates are

applied to different layers. Layer with higher data rates

might use higher order modulation schemes so that each

layer would have the same bandwidth (fig 2.2 a).

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 33

Fig2.2 (a) V-Blast encoding architecture [21]

Since distinct data stream are applied to each of the 𝑁𝑡 layer,

during each use of the channel there are 𝑁𝑡 different

modulation symbols transmitted. Therefore the space-time

code rate associated with the V-BLAST encoder is 𝑅𝑠=𝑁𝑡

and the spectral efficiency is 𝑁𝑡𝑅𝑡 (M)bps/HZ; where M is

the modulation order. In the case of V-BLAST, Loyka and

Gagnon prove that the diversity order varies from (𝑁𝑟-𝑁𝑡+1)

up to 𝑁𝑟 , depending on which layer is being decoded. We see that N*N V-BLAST only achieves a maximum diversity

gain equal to 1, compared with 𝑁𝑡𝑁𝑟 for system with full

diversity. [22, 23]

(b) HORIZONTAL-BLAST (H-BLAST)

The H-BLAST encoding architecture shown in fig 2.2 b , it

is basically similar with V-BLAST but only difference is it

includes separate conventional error encoder on each of the

transmit data stream. In this ―horizontal‖ suggest that the

encoder on each layer perform coding in the time domain,

which can be pictured as being horizontal in the picture,

compared with the space dimension that is depicted being

vertical(fig2.2 b).[24]

Fig 2.2(b) H-Blast encoding architecture[21,25]

(c) DIAGONAL-BLAST (D-BLAST)

The D-BLAST encoding architecture shown in fig 2.2 c, it is

basically similar with H-BLAST but only difference is it

includes a block after the modulator that performs stream

rotation. Let we take a example we assume that 𝑁𝑡=4 and

output are divided into blocks consisting of 𝑁𝑡 consecutive

segments, the output of the four convential encoders are vectors denoted by a, b, c and d and then output of four

encoded segments out of convential encoder 1 by 𝑎1,𝑎2,𝑎3,

and 𝑎4,the next set of four encoded segments by

a5 , a6,a7,anda8 Rather than simply passing the modulated

outputs from each encoder onto its respective antenna, the

stream rotator rotates the modulated segments in a round-

robin fashion by performing two operation: a) it distributes

consecutive sequences of 𝑁𝑡 segments from each encoder

onto each of the antenna; b) the order of the encoders that it

operated on is chosen in a circularly rotated manner rather

than simply sequentially from encoder 1 to 𝑁𝑡.

In D-BLAST, each diagonal layer constitutes a complete

code word then decoding is done layer by layer. The

advantage of this type of BLAST techniques is that the

outputs from each conventional encoder are distributed over

space which provides a grater spatial diversity (fig2.2 c).

[26]

Fig2.2(c) D-Blast encoding architecture [21, 25]

2.2.2 THREADED SPACE-TIME ENCODING (TSTE)-:

TST proposed by El Gamal et al. It was developed to enable

the construction of full rates and full diversity MIMO

transmission by combining layering ideas with constituent

space time codes. it is based on partitioning the space time signal matrix into non-overlapping threads .In this method

mixes the signal more thoroughly across the antennas than

does the D-BLAST diagonal system. The last block is a

spatial interleave, which interleaves the symbols as shown in

fig2.3 in the space time matrix and each shade shows a

International Journal of Electrical & Electronics Engineering 34 www.ijeee-apm.com

thread. We have one code word per thread, in the first

columns the symbols of each layer are not shifted and in

second columns they are shifted once in a cyclic manner. In

the third column they are shifted twice and so on. The 𝑀𝑡

*matrix A contains the symbols transmitted over the Mt transmit antennas for l symbol periods. We can describe

each layer in general by specifying a set of elements from A.

Let L= (𝐿1, 𝐿2,.. 𝐿𝑚𝑡 ) be set of indices specifying the

elements of A. Mathematically LI is defined as[27,28]

𝐿𝑖 = { ([t+i-1]𝑀𝑇 + 1,l): 0≤ 𝑡 ≤ 𝑙}

Fig2.3Threaded Space-Time encoding architecture [21, 25]

3. CONCLUSION

We have study the various types of the space-time codes

techniques in which every techniques it own advantages and

limitation like generally, in the interest of coding gain, we

prefer to use trellis codes instead of block codes within the

space-time architecture, trellis codes provides higher coding

gain but come at the cost of increased decoding complexity.

We have also study that TLST codes yielded the maximum transmit diversity. The V-BLAST which has gained a lot of

popularity because of its simplicity.

REFERENCE [1] CommWeb. Wireless industry statistics, 2001. [2] Ari Hottinen, Olav Tirkkonen, and Risto Wichman. Multi-antenna transceiver techniques for 3G and beyond. John Wiley & Sons, 2003.

[3] Theodore S. Rappaport, A. Annamalai, R. M. Buehrer, and William H. Tranter. Wireless communications: Past events and a future perspective. IEEE Communications Magazine, 40(5):148{161, May 2002. [4] B.A. Sethuraman, B. Sundar Rajan, and V. Shashidhar (October 2003). "Full-diversity, high-rate space-time block codes from division algebras". IEEE Transactions on Information Theory 49 (10)

[5]Marzetta, T.L. and Hochwald, B.M. (January 1999). "Capacity of a mobile multiple-antenna communication link in Rayleigh flat fading". IEEE Transactions of Information Theory 45 (1): 139–157.

[6] V. Tarokh and H. Jafarkhani (July 2000). "A Differential Detection Scheme for Transmit Diversity". IEEE Journal on Selected Areas in Communications 18 (7): 1169–1174. [7] http://en.wikipedia.org/wiki [8] S.M. Alamouti (October 1998). "A simple transmit diversity

technique for wireless communications". IEEE Journal on Selected Areas in Communications 16 (8): 1451–1458. [9] Siavash M. Alamouti. A simple transmit diversity technique for wireless communications. IEEE Journal on Selected Areas in Communications, 16(8):1451{1458, October1998. [10] Vahid Tarokh, Hamid Jafarkhani, and A. Robert Calderbank. Space-time block codes from orthogonal designs. IEEE Transactions on Information Theory, 45(5):1456{1467,July 1999.

[11] Vahid Tarokh, Hamid Jafarkhani, and A. Robert Calderbank. Space-time block coding for wireless communications: Performance results. IEEE Journal on Selected Areas in Communications, 17(3):451{460, March 1999. [12]Girish Ganesan and Petre Stoica. Space-time diversity using orthogonal and amicable orthogonal designs. Wireless Personal Communications, 18(2):165{178, August 2001. [13]Hamid Jafarkhani. A quasi-orthogonal space-time block code.

IEEE Communications Letters, 49(1):1{4, January 2001. [14] Babak Hassibi and Bertrand Hochwald. High-rate codes that are linear in space and time. IEEE Transactions on Information Theory, 48(7):1804{1824, July 2002. [15] Erik G. Larsson, Petre Stoica, Erik Lindskog, and Jian Li. Space-time block coding for frequency-selective channels. In IEEE International Conference on Acoustics, Speech and Signal Processing, volume 3, pages 2405{2408, May 2002.

[16] Erik Lindskog and Arogyaswami J. Paulraj. A transmit diversity scheme for channels with intersymbol interference. In IEEE International Conference on Communications volume 1, pages 307{311, June 2000. [17] Naofal Al-Dhahir. Single-carrier frequency-domain equalization for space-time block-coded transmissions over frequency-selective fading channels. IEEE Communications Letters, 5(7):304{306, July 2001. [18] Shengli Zhou and Georgios B. Giannakis. Space-time coding

with maximum diversity gains over frequency-selective fading channels. IEEE SIgnal Processing Letters,8(10):269{272, October 2001. [19] Sriram Mudulodu and Arogyaswami J. Paulraj. A transmit diversity scheme for frequency selective fading channels. In IEEE Global Telecommunications Conference,volume 2, pages 1089{1093, November 2000. [20] Vahid Tarokh, Nambi Seshadri, and A. Robert Calderbank.

Space-time codes for high data rate wireless communication: Performance criterion and code construction. IEEE Transactions on Information Theory, 44(2):744{765, March 1998. [21] INTRODUCTION TO MIMO COMMUNICATIONS BY JERRY R. HAMPTON CAMBRIDGE UNIVERSITY PRESS, 28-NOV-2013 [22] Gerard J. Foschini, Glen D. Golden, Reinaldo A. Valenzuela, and Peter W. Wolniansky. Simplifed processing for high spectral efficiency wireless communication employing multi-element

arrays. IEEE Journal on Selected Areas in Communications,17(11):1841{1852, November 1999. [23] Peter W. Wolniansky, Gerard J. Foschini, Glen D. Golden, and Reinaldo A. Valenzuela.V-BLAST: An architecture for realizing very high data rates over the rich-scattering wireless channel. In International Symposium on Signals, Systems, and Electronics,pages 295{300, September 1998. [24] Gerard J. Foschini, Dmitry Chizhik, Micahel J. Gans,

Constantinos B. Papadias, and Reinaldo A. Valenzuela. Analysis and performance of some basic space-time architectures. IEEE Journal on Selected Areas in Communications, 21(3):303{320, April2003. [25] SPACE-TIME CODES AND MIMO SYSTEMS BY MOHINDER

JANKIRAMAN ARTECH HOUSE, 01-JAN-2004. [26] Gerard J. Foschini. Layered space-time architecture for wireless communication in a fading environment when using

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multiple antennas. Bell Labs Technical Journal,1(2):41{59, September 1996. [27] Hesham El Gamal and Jr. A. Roger Hammons. A new approach to layered space-time coding and signal processing. IEEE Transactions on Information Theory, 47(6):2321{2334, September

2001. [28] Hesham El Gamal and Mohamed Oussama Damen. Universal space-time coding. IEEE Transactions on Information Theory, 49(5):1097{1119, May 2003.

AUTHORS

Maninder Singh is following M.Tech

from Indo Global College of

Engineering, India. He has completed B.Tech from IGCE, Mohali (Punjab),

India in the year 2011. He has two

year of educational expertise.

Working as Assistant Professor (ECE)

at indo global college of Engineering, Abhipur (Mohali) since

June-2012.His areas of interest are wireless and mobile

communication, Optical communication.

Hardeep Singh Saini obtained his

Doctorate degree in Electronics and

Communication Engineering in 2012. He holds Master‘s degree in Electronic

and communication from Punjab

technical university, jalandhar passed in

2007. His total experience is 15 year,

presently, working as Professor (ECE) and Associate Dean

Academic at Indo Global college of Engineering, Abhipur

(Mohali), PUNJAB (INDIA) since June-2007. He is author

of 5 books in the field of communication Engineering. He

has presented 21 papers in international /national

conferences and published 30 papers in international

journals. He is a fellow and senior member of various prestigious societies like IETE (India), IEEE, UACEE,

IACSIT and he is also editorial member of various

international journals.

IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426

Optimization of Transmission Schemes in

Energy-Constrained Wireless Sensor

Networks1Vivek Rana,

2Jaspal Singh,

3Leena Mahajan

1,2Rayat Institute of Engineering & Information Technology,Railmajra, Punjab, India. 3Indo Global college of Engineering, Abhipur,Distt. Mohali,Punjab,India

1ranavivek01@gmail.com, 2jaspal_116@yahoo.co.in, 3leenamahajan1997@gmail.com

Abstract- This paper reviews medium access control

(MAC) in wireless sensor network (WSN),and different management methods to save energy.MAC protocol

controls how sensors access a shared radio channel to

communicate with neighbours. This paper discusses design

trade-offs with an emphasis on energy efficiency, latency,

fairness and throughput. One mechanism used to reduce

energy expenditure is to periodically turn off the radio

receivers of the sensor nodes in a coordinated manner. S-

MAC may require some nodes to follow multiple sleep

schedules causing them to wake up mmore often than other

nodes. A typical node in WSN consists of one or more

sensors, embedded processors, moderate amount of

memories and transmitter/receiver circuitry. These sensors are battery powered and recharging of these nodes is very

expensive and normally not possible. The proposed

modification in MAC protocol solves the energy

inefficiency caused by idle listening, control packet,

overhead, and overhearing taking nodes latency into

consideration based on network traffic. The modified

version improves the energy efficiency, latency and the

throughput and hence increases the life span of a wireless

sensor network. Simulation experiments have been

performed to demonstrate the effectiveness of the proposed

approach. This protocol has been simulated in Qualnet 5.0.

Keywords- Wireless Sensor Network, Medium Access

Control, Energy Efficiency , latency, throughput, fairness.

I. Introduction

A wireless sensor network (WSN) of spatially distributed

autonomous sensors to monitor physical or environmental

conditions, such as temperature, sound, pressure, etc. and to

cooperatively pass their data through the network to a main

location. The more modern networks are bi-directional, also enabling control of sensor activity. The development of

wireless sensor networks was motivated by military

applications such as battle field surveillance; today such

networks are used in many industrial and consumer

applications, such as industrial process monitoring and

control, machine health monitoring, and so on.

A WSN generally consists of a host or ―gateway‖ that

communicates with a number of wireless sensors via a radio

link. Data is collected at the wireless sensor node,

compressed, and communicated to the gateway directly or,

if required, uses other wireless sensor nodes to forward data

to the gateway. The gateway then ensures that the data is input into the system. The main function of a wireless

sensor network (WSN) is to collect data from environment

and send it to a reporting site where the data can be

observed and analyzed Each wireless sensor is considered a

node and presents wireless communication capability, along

with a certain level of intelligence for signal processing and

networking data. Depending on the type of application, each

node can have a specific address. Figure 1 represents a

generic block diagram of a node. It usually comprises a

sensing unit, a microcontroller to process data, and a RF

block for the wireless connection. Depending on the

network definition, the RF block can function as a simple transmitter or transceiver (TX/RX). When designing the

nodes, it is very important to pay attention to the current

consumption as well as the processing capability. The

microcontroller‘s memory is very dependent of the software

stack used.

Fig.1: Generic block diagram of a node of a WSN.

Wireless Sensor Networks (WSNs) are an important new

class of networked system.

Dealing with both scale and density is hard enough in ideal environments. Unfortunately, we don‘t have the luxury of

ideal environments with sensor networks. Because sensor

networks are intended to monitor the physical world, they

must often be deployed in natural and uncontrolled

environments. No longer can we assume the carefully

controlled temperature, abundant power, and human

monitoring of server rooms and data centers. Instead,

wireless sensor networks must be designed to operate while

no external power is connected, unattended, irregularly

connected (radios may be turned off for significant periods

of time to conserve power), and uncontrolled environment

[1]. MAC protocols have a significant effect on the function of

WSN. MAC protocol, which builds bottom infrastructure in

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 37

sensor network systems, decides how to use wireless

channel and allocate limited wireless communication

resources for sensor nodes. MAC protocol, one of the key

network protocols that ensure effective communication in

sensor network, is in the bottom part of the sensor network

protocol and has a great impact on the performance of

sensor network [6].

II. RELATED WORK

A. Proposed S-MAC Protocol Design Challenges

It is necessary to establish communication links between

nodes because a great number of sensor nodes are

distributed to the medium in Wireless Sensor Networks. For this reason, MAC protocol has two aims in WSNs. The first

is to build a sensor network infrastructure. The second is to

share the communication medium in a fair and efficient way

[8].

Attributes that should be taken into consideration in the

design of MAC protocol are listed on below :

Energy efficiency: Energy efficiency is the most important

issue when designing a new MAC protocol in WSNs

because the network‘s lifetime is determined by the nodes‘

energy.

Latency: The elapsed time for sending a MAC-layer data

packet successfully is called ―Latency‖.

Throughput: The ratio of the messages served by

communication systems is called ―Throughput‖.

Robustness: Robustness is composed of the attributes

including reliability, usability, and durability. It shows the

protocol‘s degree of resistance to errors and false

information.

Scalability: Capability of communication system regardless

of the number of sensor nodes performing a transaction and

the size of the network is called ―Scalability‖.

Stability: The ability of communication system to handle

the issue of traffic congestion in the medium that changes

constantly is called ―Stability‖. A stable MAC protocol

should handle sudden loads that can exceed maximum

channel capacity.

Fairness: Bandwidth is limited in most of WSNs

applications, but the base station must receive data equally

from all the nodes. Channel capacity should be fairly shared among the nodes without reducing the efficiency of the

network.

The main goal in our S- MAC protocol design is to reduce

energy consumption, while supporting good scalability,

fairness and collision avoidance. Our protocol tries to

reduce energy consumption from all the sources that we

have identified to cause energy waste. To achieve the design

goal, we have developed the S-MAC that consists of three

major components: periodic listen and sleep, collision and

overhearing avoidance, and message passing. A

modification of the protocol is then proposed to eliminate the need for some nodes to stay awake longer than the other

nodes. The modified version improves the energy

efficiency, latency, fairness and the throughput and hence

increases the life span of a wireless sensor network.

Wireless sensor networks use battery-operated computing

and sensing devices [3]. We expect sensor networks to be

deployed in an ad hoc fashion, with nodes remaining largely

inactive for long time, but becoming suddenly active when something is detected. These characteristics of sensor

networks and applications motivate a MAC that is different

from traditional wireless MACs such as IEEE 802.11 in

several ways [2, 4]: energy conservation and self-

configuration are primary goals, while per-node fairness and

latency are less important. S-MAC uses a few novel

techniques to reduce energy consumption and support self-

configuration. It enables low-duty-cycle operation in a

multi-hop network. Nodes form virtual clusters based on

common sleep schedules to reduce control overhead and

enable traffic-adaptive wake-up. S-MAC uses in-channel

signaling to avoid overhearing unnecessary traffic. Finally, S-MAC applies message passing to reduce contention

latency for applications that require in-network data

processing.

B. S-MAC Protocol

S-MAC [9] is a CSMA –based MAC protocol designed with

a modified IEEE 802.11. Its primary goal is power

consumption. S-MAC supports message transition so that

large-sized packets can be sent more efficiently. The

innovations in this protocol are periodical listening,

reducing collision, preventing unintentional receiving, and

message transition. Nodes generally sleep instead of continuously listening to the medium. Listening and

sleeping times are stable and periodic. There should be a

strict synchronization so that the nodes can move together.

The timing diagram of S-MAC is shown in Figure 2.

Fig. 2. Timing diagram of S-MAC

The Sensor MAC (S-MAC) protocol was introduced in [5]

to solve the energy consumption related problems of idle

listening, collisions, and overhearing in WSNs using only

one transceiver. S-MAC considers that nodes do not need to

be awake all the time given the low sensing event and

transmission rates. S-MAC [3] reduces the idle listening problem by turning the radio off and on periodically. Nodes

are synchronized to go to sleep and wake up at the same

time. In order to address the issue of synchronization over

multi-hop networks, nodes broadcast their schedules to all

its neighbors. This is performed sending a small SYNC

frame with the node schedule periodically. S-MAC divides

time in two parts: the active (listening) part and the inactive

(sleeping) part. The active part is divided at the same time

in two time slots. During the first time slot, nodes are

expected to send their SYNC frames to synchronize their

schedules. The second time slot is for data transmission in

which the S-MAC protocol transmits all frames that were queued up during the inactive part. In order to send SYNC

frames over the first time slot or RTS–CTS–DATA–ACK

frames over the second time slot, nodes obtain access to the

media utilizing the same contention mechanism included in

IEEE 802.11, which avoids the hidden terminal problem

and does a very good job avoiding collisions too. However,

nodes using the IEEE 802.11 protocol waste a considerable

amount of energy listening and decoding frames not

intended for them [4]. In order to address this problem, S-MAC allows nodes to go to sleep after they hear RTS or

CTS frames. During the sleeping time, a node turns off its

radio to preserve energy.

Fig.3: S-MAC frame

C. Problems with S-MAC

The following two problems have been identified in S-MAC

[3] protocol with multiple schedules.

1. Longer listen period

2. Sleep delay

1. Longer listen period

While choosing and maintaining the listen and sleep

schedule some nodes may have to keep wake during the

listen time of more than one schedule [3]. This happens, for

example, if a node,

(A): Before

(B): After

Fig.4: Sleep schedule before and after node M join the network

When it starts up, finds some of its neighbors following one schedules and the rest following another. The nodes

following a shared schedule are said to form a virtual

cluster. Figure 4 shows an example of this situation. Before

node M starts up, two isolated virtual clusters of nodes

exist. Nodes A, B and C follow one schedule (schedule 1);

and nodes X, Y and Z follow another schedule (schedule 2).

The circle around a node indicates the communication range

of the node. When M starts, during its initial listening

spanning a synchronization period, it receives sync frames

corresponding to both the schedules. M will then adopt one of the schedules (e.g. schedule 2) as its own, and announce

this schedule in its sync frames. However, it will also have

to wake up during the listen time of the other schedule.

Thus M has higher duty cycle, and consumes more energy.

2. Sleep delay

Sleep delay introduce extra end to end delay called sleep

delay [3]. Sleep delay increases communication latency in

multihop networks, as intermediate nodes on a route do not

necessarily share a common schedule. In a nutshell, the

difficulty is to make a trade off between sleep delay and optimal active periods.

D. Proposed Modification in S-MAC

In this section we propose a modification of the S-MAC

protocol. The following features were included in the S-

MAC design:

RTS/CTS for hidden terminal problem.

Both virtual and physical carrier sense.

Back off and retry.

RTS/CTS/ACK.

Broadcast packets are sent directly without using

The RTS/CTS reserves the medium for the entire

message.ACK is used for immediate error

recovery.

Node goes to sleep when its neighbor is

communicating with another node. Each node

follows a periodic listen/sleep schedule.

At boot up time each node listens for a fixed Sync

period and then tries to send out a sync packet. It

suppresses sending out of sync packet if it happens

to receive a sync packet from a neighbor and follows the neighbor's schedule.

A node can choose its own schedule instead of

following others, the schedule start time is user

configurable.

Neighbor Discovery: in order to prevent that two

neighbors cannot find each other due to following

complete different schedules, each node

periodically listen for a whole period of the

SYNCPERIOD.

Duty cycle is user configurable.

III. RESULT AND DISCUSSION

The objective of this discussion is to compare the S-MAC

and the modified proposed S-MAC protocol in terms of

energy efficiency, latency, fairness, security and throughput.

We need to have set of protocols to perform successful

communication among different nodes. There are more

steps for design a MAC protocol. First, researchers have to

decide that in which application do they use this protocol.

Because there are more priority such as energy efficiency,

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 39

latency, fairness, throughput, security. If your first priority

is energy efficiency, you can neglect to more security.

Because, each work for security causes that consumption

and delay. Otherwise, if you develop a protocol which will

be use in military or healthcare applications, you have to

provide security requirements. In order to meet the

application level security requirements, the individual nodes must be capable of performing complex encrypting and

authentication algorithms. Long mechanism of encryption

and decryption should not be kept as they consume more

energy. In WSNs, energy efficiency is the main task. After

measuring the effect of the parameters like power, lifetime

of sensor network, memory, security and type of radio

communication on different protocols, it can be concluded

that these evaluation parameters should be kept in mind

while designing MAC protocol. Simulation results of the

WSN models are presented under varying network load

conditions followed by performance comparisons and

analysis.

A. Measurement of Energy Consumption

We measured the energy consumption in the ten-hop

network. In each test, the source node sends a fixed amount

of data, 20 messages of 100-bytes each. Figure 5 shows that

S-MAC with periodic sleep consumes much more energy

over MAC without sleep, but the proposed MAC achieves

better energy efficiency than the S-MAC protocol.

FIG.5: Energy Consumption

B. Measurement of Average Message Latency

Since S-MAC makes the trade-off of latency for energy

savings, we expect that it can have longer latency under

both the high and low traffic loads due to the periodic sleep

on each node as shown in figure 6(A) and figure 6(B). We consider two extreme traffic conditions, the lowest traffic

load and highest traffic load. Under the lowest traffic load,

the second message is generated on the source node after

the first one is received by the sink. To do this, a

coordinating node is placed near the sink. When it hears that

the sink receives the message, it signals the source directly

by sending at the highest power. In this traffic load, there is

no queuing delay on each node. Compared with the MAC

without sleep, the extra delay is only caused by the periodic

sleep on each node. Under the highest traffic load, all

messages are generated and queued on the source node at the same time. So there is a maximum queuing delay on

each node including the source node. The latency of the

proposed MAC protocol is nearly equal to that of MAC

without periodic sleep but still it doesn‘t reach the shortest

latency.

FIG.6 (A): Average Message Latency under the lowest traffic load

FIG. 6(B): Average Message Latency under the highest traffic load

C. Measurement of Throughput

Just as S-MAC may increase latency, it may also reduce the

throughput. Therefore we next evaluate throughput in the

same 10-hop network. We first consider throughput for the highest traffic load, which is the same as that when

measuring the latency in the highest traffic load. It delivers

the maximum possible number of bytes of data in a unit

time. The results in figure 5 show that for S-MAC as well as

for proposed S-MAC, throughput drops as the number of

hops increases, due to the RTS/CTS contention in the

multihop network.

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FIG.7: Throughput over 10-hops under varying traffic loads.

IV. FUTURE SCOPE OF WORK

During this work we realized that the MAC protocols for

the wireless sensor networks are a hard and extensive area.

Although modification in S-MAC protocol has been

proposed, there is possible future work for system performance optimization. Therefore, some of the planned

work has to be rationalized away for future work. We see

clear paths for future work:

Verification through implementation and

extensive simulations.

Formal descriptions to address other type of MAC

protocols and extension of components.

Cross layer optimization is an area that needs to be

explored more extensively.

REFERENCES

[1] Akyildiz, I.F. ; Su, W. ; Sankarasubramaniam, Y. ;Cayirci, E. (2002)

―A survey on sensor networks‖, IEEE Communications Magazine 40.8

(2002) 102-114.

[2] Brenner, Pablo. (1996) ―A Technical Tutorial on the IEEE

802.11Protocol‖, Breezecom Wireless Communications, July 1996.

[3] Cui, S. ; Goldsmith A. J., and Bahai A., ―Energy-constrained

modulation optimization,‖ IEEE Trans. Wireless Commun., vol. 4, no. 5,

pp. 2349–2360, Sep. 2005.

[4] Ghosh, S.; Veeraraghavan, P.; Singh, S.; Zhang, L. (2009)

―Performance of a Wireless Sensor Network MAC Protocol with a Global

Sleep Schedule‖ International Journal of Multimedia and Ubiquitous

Engineering Vol. 4, No. 2, April, 2009

[5] IEEE Standard 802.11. (1999) ―Wireless LAN Medium Access Control

(MAC) and Physical Layer (PHY) Specifications‖, 1999.

[6] Kodialam, M and Nandagopal T., ―Characterizing achievable rates in

ulti-hop wireless networks: The joint routing and scheduling problem,‖ in

Proc. ACM MobiCom‘03, Sep. 2003, pp. 42–54.

[7] Labrador, M. A.; Wightman, P. M. (2009) ―Topology Control in

Wireless Sensor Networks‖ Springer, USA.

[8]Pottie, G. and Kaiser, W. ―Wireless sensor networks,‖ Communication.

ACM, vol. 43, no. 5, pp. 51–58, 2000.

[9] Ye, W.; Heidemann, J. ; Estrin, D. (2002) ―An Energy-Efficient MAC

Protocol for Wireless Sensor Networks‖, Twenty-First Annual Joint

Conference of the IEEE Computer and Communications Societies

(INFOCOM) 3 (2002) 1567-1576.

AUTHORS

Vivek Rana graduated in Electronics

& Communication Engineering from

Rayat Institute Of Information and

Technology, Railmajra, Punjab. Now

he is a student of M-Tech in

Electronics & Communication

Engineering in Rayat institute of

information and Technology Railmajra, Punjab. His active

research interests include wireless sensor network, Wireless

communication, computer networking & semiconductor

devices.

Jaspal Singh graduated in

Electronics & Communication

Engineering from Baba Banda Singh

Bahadur Engineering College,

Fatehgarh Sahib, Punjab. He has

received his M-Tech degree in

Electronics & Communication

Engineering from Thapar Institute of

Engineering and Technology, Patiala, Punjab. He is

working as Associate Professor and HOD in ECE

department in Rayat Institute of Engineering and

Technology, Railmajra, Punjab. He is a life member of

ISTE. His active research interests include intelligent

sensor network, wireless sensor network, Optical wireless

communication, Wireless communication & network,

microwave engineering, semiconductor devices

Leena Mahajan graduated in

Electronics & Communication

Engineering from Institute of

Electronics and Telecommunication

Engineering , New Delhi. She has received her M-Tech degree in

Electronics & Communication

Engineering from Baba Banda Singh

Bahadur Engineering College,

Fatehgarh Sahib, Punjab. She has a very rich experience of

13 years in Telecom sector. She has served many

organizations like Himachal Futuristic Communications

Limited, Chambaghat, Himachal Pradesh, India, Punjab

Communications Limited, Mohali, Punjab, India. Presently

she is working as Assistant Professor in Indo Global

College of Engineering, Abhipur, Punjab,India. She is a

corporate life member of IETE. She is guiding many thesis of M Tech students. She has published many national and

international papers on WSN and Managememt . Her active

research interests include intelligent sensor network,

wireless sensor network, Optical wireless communication,

Wireless communication network & switching devices.

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Mod S-MAC

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 41

IJEEE, Vol. 1, Spl. Issue 1 (March 2014) e-ISSN: 1694-2310 | p-ISSN: 1694-2426

Effect on Channel Capacity of Multi-User MIMO

System in Crowded Area

Vinay Thakur1, Surinder Kumar Rana

2, Abhishek Thakur

3

1,2Electronics & Communication Department, Sri Sai University, India 3Electronics & Communication Department, Indo Global College of Engineering, Punjab, India

1Vinay.rajput1@gmail.com,

2Sindu.97@gmail.com

INTRODUCTION

Multiple-Input Multiple-Output (MIMO) and Multi-User

MIMO (MU-MIMO) systems have been expected to

improve the channel capacity over a limited bandwidth of

existing networks [1], [2]. The effects on channel capacity of

Single-User MIMO (SU-MIMO) systems in urban scenarios

have been previously studied [3]. It has been clarified that

the larger number of antennas cannot contribute the improvement on the channel capacity in urban SU-MIMO

scenarios due to very high spatial correlation. MIMO is also

called by some people my moh and me moh by other people,

for the better communication we mostly use multiple

antennas at receiver and transmission end. In the latest

technology there are several forms of the antennas. In this

paper, we focus on the MU-MIMO transmission because it

can discriminate multiple users by the difference of Angle of

Arrival (AoA). We compare the Multi Access Channel

(MAC) capacity in uplink with the channel capacity in SU-

MIMO by setting the total numbers of transmitting and

receiving antennas of SU-MIMO and MU-MIMO to be the same. Multiple input and multiple output technique has call

the notice in wireless communications, because it gives a

hike in data output and range without any need of any other

external power and any change in bandwidth. It attains this

target by giving the same total transmitting power over the

antennas to achieve the spectral efficiency and to attain a

gain that improves the reliability by reducing the fading

effect. When the same numbers of antenna elements are

used, the better performance is obtained with MU-MIMO in

urban scenarios, unlike identical independent distributed

(i.i.d.) channels which are generally assumed in MIMO transmission. We also clarify an interesting relationship

between the channel capacity improvement of MU-MIMO

compared with SU-MIMO and a path visibility.

A. Antenna and User Models

The antennas and the user are simulated through fullwave

EM simulations that are performed with a three dimensional

(3D) solver, FEKO [12]. The MIMO handset has two classic

single-band PIFAs designed co-polarized to each other and

both resonate at 2.6 GHz. We consider three usage scenarios:

i) Head only (H), ii) voice scenario with the user head and

hand (HH); and iii) data scenario (D) with the user‘s two hands. The examined usage scenarios are

shown in Fig. 1(a)-(c) where the phantom head and the hand

models are used to simulate the user.

B. Antenna Efficiency

An important factor in characterizing antennas is the

radiation pattern and hence, gain and efficiency of the

antenna. The antenna patterns and efficiency definitions are

not obvious and cannot be directly derived from

conventional pattern descriptions when the antenna is placed

in the vicinity of or on a lossy medium. This is due to losses

in the medium that cause waves in the far-field to attenuate

more quickly and finally to zero. The antenna efficiency is

proportional to its gain [11] (,) (,) GDθφ =η⋅ θφ. (2) In (2) ηis the total efficiency factor and D(,) θφ is the antenna

directivity, which is obtained from the antenna normalized

power pattern that is observed in the far-field. An antenna

within a handset, for example, and/or in the vicinity of a user

would have different efficiency from an antenna in free

space due to changes in the far-field radiation pattern. Fig. 2

shows the total far-field pattern of the antenna in the

different usage scenarios described in Fig. 1. The difference

in the patterns among the different scenarios is obvious.

These differences arise from the change in the electric field

distributions at varying distances from the body or any other obstacles in the communications channel.

I. ANALYSIS MODEL

The urban propagation model employed in this paper is

represented in Fig. 1. This model is composed of 64 blocks

of 50m×50m. Each block is composed of 4 buildings. The

road width is 20m. The buildings are assumed to be

constructed of concrete and the relative

dielectric constant and conductivity are set to 5 and 0.01S/m,

respectively. The uplink scenario of (M1+M2)×NMU-

MIMO systems (from MT to BS) are considered. The

characters M1, M2, and N respectively represent the

numbers of antenna elements of the first MT, the second

MT, and the BS. It is noted that M1+M2is supposed to be

not greater than N. A linear-array BS is located at the top of

a building on one side of the model as shown in Fig. 1. Since

an accurate reflection or diffraction cannot be obtained at the

edge of analysis model, the MTs are assumed to move independently on the road in an area of 280m×280m around

the center of the model along the broken lines in Fig. 1 at the

height of 1.5m. The MT antennas are set in a symmetrical

array at half-wavelength (λ/2) spacing. The propagation

characteristics between MT and BS are then calculated by

using the ray-tracing method. The distribution of the height

of buildings is assumed following chi-squared

distribution:χ2(k), with kdegrees of freedom (DoF) which is

herein set to 5. The minimum height of these buildings is set

to 4m. The height of building:h, can be expressed as [4]

() 4, =+ hkχ

The carrier frequency is 3GHz. The numbers of reflection and diffraction are 30 and 2, respectively. The channel

response matrices are obtained from the complex received

voltage matrices which are calculated at intervals of 14m in

length along the broken lines in Fig. 1. The uplink scenario

is considered. It is assumed that the Channel State

Information (CSI) between the transmitter and receiver is not

known by the MT. Whenthe transmitter does not know the

CSI, the channel capacity of SU-MIMO can be obtained in

the units of bps/Hz as [1]

In cases of MU-MIMO, the analysis of channel is commonly

referred to the MAC [5]. The MAC capacity (CMAC) is

considered as the total channel capacity which the BS

antenna can receive from the MTs moving in the propagation area. In MAC channel, the BS can estimate all the CSI from

the MTs. In cases of 2-user MIMO systems, this CMACcan

be obtained by a substitution of the combined CSI (HMAC)

shown in Fig. 2 into (3).

II. FUNCTION OF MIMO

Three main categories of MIMO, Precoding ,Spatial

multiplexing and Diversity coding. Precoding is multistream

beam forming and considered to be all spatial processing. In single stream beam signal is transmitted with appropriate

gain, phase and maximized power at receiver. Its advantages

are to increase received signal gain with all signals get add

up from different antennas & reduce multipath fading. In

Line of sight, beam formed is directional but conventional

beam are not good analogy in cellular network ,with multiple

antenna, the transmitting beam formed cannot maximized

signal level at receiving antenna. So precoding is used and

requires channel state information (CSI) at transmitter and

receiver. In spatial multiplexing, splits high rate signal

stream into multiple lower rate signals ,each signal stream is

transmitted from different transmitting antennas at same frequency channel and required MIMO antenna

configuration. If these signals arrive at the receiver antenna

array with sufficiently different spatial signatures and the

receiver has accurate CSI, it can separate these streams into

(almost) parallel channels. It increase channel capacity at

higher signal-to-noise ratios (SNR) and maximum number of

spatial streams is limited by less number of antennas at the

transmitter or receiver. It can be used without CSI at the

transmitter, but can be combined with precoding if CSI is

available. It can also be used for simultaneous transmission

to multiple receivers, known as space-division multiple access or multi-user MIMO, in which case CSI is required at

the transmitter.

Channel Capacity Characteristics of Urban MU-MIMO

Systems

The channel capacity of urban SU-MIMO has been

evaluated [3]. It has been clarified that the channel capacity

of SU-MIMO is deteriorated compared with the i.i.d. cases

due to a very high spatial correlation in urban propagation

environment. Hence, to reduce the effect of the spatial

correlation, the MU-MIMO transmission is introduced.

Figure 3 shows the effects of model configurations on the channel capacity of (2+2)×4 MU-MIMO compared with 4×4

SU-MIMO. The results present significance, since there are

situations that CMAC> CSU, i.e. the MU-MIMO

transmission presents effectiveness. These results confirm

that the channel capacity characteristics of MU-MIMO are

greatly different from those in neither indoor nor i.i.d.

scenarios [6]. These are supported by Fig. 4. The average

spatial correlation between users of (2+2)×4 MU-MIMO

which two MTs moving independently in the propagation

area is much lower than the average spatial correlation

between each antenna element of 4×4 SU-MIMO which all MT antenna elements always stay closely. Since the spatial

correlation becomes low, its effect on the channel capacity is

also deteriorated.

www.ijeee-apm.com International Journal of Electrical & Electronics Engineering 43

From the view of receiving antenna (BS), the AoA-diff. is

definedas the difference of angle which the signal from each

MT arrives at BS.Its effect on the channel capacity is

indicated. Figure 5 shows the CMACand CSUat each

AoAdiff. In cases of MU-MIMO, when the AoA-diff is

increased or the MTs stay farther from each other, the channel capacity is relatively increased. Even if the BS is

low mounted (50m) which MU-MIMO transmission is not

much more effective than SU-MIMO (see Fig. 3), the

channel capacity is also increased when two MTs are far

apart which the correlation becomes low. Moreover, when

the MTs stay at very near locations, or the AoA-diff is small,

(2+2)×4-MU-MIMO channels can be approximately

regarded as 4×4 SU-MIMO, and the channel capacity

becomes low due to high correlation.

Figure 6 shows the channel capacity improvement of MU-

MIMO over SU-MIMO. The curves show the ratio between

CMAC and CSU. The intersections between these curves

and the horizontal dashed line indicate the turning points

which CMAC becomes greater than CSU(CMAC/CSU> 1).

As the average building height is higher, the turning points

relatively present at a higher BS antenna height. For a clear

discussion, the path visibility defined as the probability that the direct wave can be received at the receiving antenna or

Line-o Sight (LoS) exists [3], is considered. Figure 7 shows

the effect of the path visibility on the characteristics of

CMAC/CSU. As the results, along the increment of the path

visibility, the ratio between CMAC/CSUis relatively

increased, because in urban propagation scenario which the

spatial correlation is very high, the independent movements

of users in MUMIMO can reduce the spatial correlation.

That is the reason why the MU-MIMO transmission can

present the effectiveness while the SU-MIMO cannot.

Furthermore, considering the fitting curve in Fig. 7, it is

clarified that CMAC becomes greater than CSU, when the path visibility is about 13 percent. That is to say, to obtain an

effectiveness of urban wireless communication, not only the

MU-MIMO transmission is supposed to be employed, but

also the BS antenna should be mounted at the height so as

the path visibility is greater than 13 percent. This result will

be useful when considering the installation of the BS in

urban SU/MU-MIMO systems.

III. CONCLUSION

Throughout this paper, the channel capacity characteristics

of urban SU-MIMO and MUMIMO considering the uplink

scenario were studied. The MU-MIMO transmission was

introduced to reduce the spatial correlation. The MAC

capacity in 2-user 2×4 ((2+2)×4) MU-MIMO was compared

with the channel capacity in 4×4 SU-MIMO. It was clarified

that the spatial correlation between users of MU-MIMO

which two MTs moving independently in the propagation

area was much lower than that of SU-MIMO which all MT

antenna elements stayed closely all the times. Its effect on the channel capacity was consequently deteriorated. By the

definition of AoA-diff, it was shown that when the MTs

stayed farther from each other which the spatial correlation

became low, the channel capacity was increased. Moreover,

when the AoA-diff was small or the MTs stayed at very near

locations, (2+2)×4 MU-MIMO channels could be

approximately

regarded as 4×4 SU-MIMO. Finally, it was shown that the

channel capacity improvement of MU-MIMO over SU-

MIMO was relatively increased along with the increment of

the path visibility.

REFERENCES.

[1] S. Hemrungrote, T. Hori, M. Fujimoto, and K. Nishimori, ―Effects of path visibility on urban MIMO systems,‖ Proc. ISAP2009, Bangkok, Thailand, pp.157-160, Oct. 2009..

[2] Y. Ito, "The distribution of height and width of buildings", in Radiowave Propagation Handbook, Eds. Japan: Realize Inc., 1999, pp. 342–349, Realize Inc., Japan, 1999.

[3] A. Goldsmith, S.A. Jafar, N. Jindal, and S. Vishwanath,

―Capacity limits of MIMO channels,‖ IEEE J. Commun., vol.21, no.5, pp.684 -702, Jun. 2003.

[4] P. Kildal, K. Rosengren, ―Correlation and capacity of MIMO systems and mutual coupling, and diversity gain of their antennas: simulations and measurements in a reverberation chamber,‖ IEEE Communications Magazine, Dec. 2004.