Seminar on

“Design of Efficient Mobile Femtocell by Compression and Aggregation Technology in Cellular Network ”

Presented By Mr. Virendra A. Uppalwar

IV Semester M. Tech. (Communication Engineering)

Mr. Akshay P. NanoteAssistant Manager

Gupta Energy Pvt.Ltd. Deoli, Wardha

Prof. S.M. Sakhare Ass. Professor

P.G. Department of Ele. & Comm. Suresh Deshmukh College of Engineering,

Selukate, Wardha.

P. G. Department of Electronics (Communication Engineering)Suresh Deshmukh College of Engineering, Selukate, Wardha.

Under the Guidance of Guide Co-Guide

Contents• Introduction• Literature Survey• Problem Definition• Objectives• Network Formation• Project Simulation

Simulation ParametersSimulation Process

• Results And Analysis• Conclusion • Future Scope• References

Introduction• Femtocell is a small size base station, low power domestic access point, cellular base station.

• Femtocell allows service providers to extend service coverage in indoors at the edge and Improve indoor coverage and capacity.

• It connects to the service providers network via broadband , such as DSL or Cable, Support 2 to 6 active User’s.

• When users walk outside or out of range, calls are automatically handed over to the external mobile network.

• Unlike Wi-Fi access points, Femtocells operate using licensed spectrum and thus must be supplied and operated in conjunction with the mobile operator.

• The concept is applicable to all wireless standards, including UMTS, GSM, CDMA and Wi-MAX solutions also for LTE.

• In 3GPP Terminology A Home Node B (HNB) is a 3G Femtocell. A Home eNode B (HeNB) is a 4G Femtocell.

Small Cell ?

• In homes and buildings where coverage decreases considerably as soon as you go indoors.

• It has become necessary to work on new technology that will facilitate calling coverage both indoor and outdoor.

• Improvement Small Cell technology could be the answer.

Evolution of Cellular System : Cell Size

Literature Survey

• Title of Paper 1 : “A Survey on Power Control Technique in Femtocell N/W”

• Name of Author : Mohamod Ismail , Rosdiadee Nordin

• Publication : IEEE, Journal of Communications Vol. 8, No. 12, December 2013.

• The Objective of paper : Focus on power control technique in Femtocell.

Idea Represented In this paper , Femtocell base stations (FBS’s) perform Self-Optimization function , that

continually adjust the transmit power. So, the femtocell coverage does not leak into an outdoor area while sufficiently covering the indoor femtocell area.

Methodology Paper explains the different power control technique. a) Fixed HeNB power setting. b) Location based power control scheme. c) Power control based interference avoidance schemes.

Conclusion Femtocell Base Stations (FBS’s) is a small low power device, but it should be able

to handle the complexity of different power control techniques as highliated in literature.

• Title of Paper 2 :

“Energy Efficient Power Management for 4G Heterogeneous Cellular Networks”

• Name of Author : Xiang Xu, Gledi Kutrolli, Rudolf Mathar.

• Year of Publication : 1st International Workshop on Green Optimized Wireless Network , GROWN’2013.

• The Objective of paper : Explain energy efficient power management technique in Femtocell.

Idea Represented Authors explains how Heterogeneous n/w is promising solution to improve the energy

efficiency of cellular system. Methodology

Using user & service classification , the proposed algorithm balances the N/W coverage , average data rate & energy consumption. Algorithm Used :

a) Resource Allocation Algorithm b) Power control algorithm with adaptive data rate offset.

Conclusion

The energy efficiency of the proposed scheme is much higher than the conventional scheme with less HUE’s per femtocell. The simulation result confirms the superiority of proposed algorithm in energy efficiency & coverage.

• Title of Paper 3 : “On the Potential of Handover Parameter Optimization for Self-Organizing Networks”

• Name of Author : Pablo Munoz, Raquel Barco, Isabel de la Bandera.

• Year of Publication : IEEE, Transactions on Vehicular Technology, Vol.62, No.5 June 2013.

• The Objective of paper : Explain HO parameter optimization for Self – Organizing network.

Idea Represented Authors explain the SON , with one of the important field of SON i.e. Handover

process in mobile N/W’s. Self Organizing N/W (SON’s) aim to raise the level of automated operation in next-generation N/W’s.

Methodology In this paper , a sensitivity analysis of the two main HO parameters i.e. the HO

margin (HOM) and the time–to-trigger (TTT) , is carried out for different system load levels and user speed in LTE. In this case, different parameter optimization levels like N/W-wide, call wide & call pair wide and the impact of measurement errors have been considered.

Conclusion Result of this sensitivity analysis show that tuning HOM is an effective

solution for HO optimization in LTE N/W’s . In addition , the adjustment of TTT does not provide greater benefits that the obtained by adjusting HOM.

• Title of Paper 4 : “Efficient SON Handover Scheme for Enterprise Femtocell Networks”

• Name of Author : Chaganti Ramarjuna, Shaikh Asif Ahammed, Riddhi Rex.

• Year of Publication : IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) 2014

• The Objective of paper : Provide efficient SON handover scheme for Femtocell network.

Idea Represented

In this paper authors propose an efficient SON handover scheme to mitigate (minimize) unnecessary handovers. The proposed approach uses building information and estimated UE (user equipment) position for making handover decision.

Methodology

In this paper authors explains , the SON (Self-Organizing-Network) is an automation technology designed to make the planning, configuration & management of mobile N/W. SON has a Plug-n-Play feature i.e.(connect and disconnect automatically) which is very important in next generation communication industry.

Conclusion

The simulation results show that SON HO scheme achieves 31.5% improvement in reducing HO delay compared to traditional HO scheme.

Overall Conclusion• As surveying all this four papers , we see that , the

SON triggers the HO whenever necessary, because of that increment in femtocell power efficiency.

• Ultimately improvement in power efficiency reduces the radiation power.

• As the SON follows user it provide a better network coverage and improve the spectral efficiency.

• As the cellular world expand people potentially requesting diverse data services such as web browsing, video streaming, and gaming.

• Users inside a building faces network problem, as the reports says 26% calls place at home, 57% mobile usage at indoor, 75% of 3networkG traffic to originate in- building as 2013.

• In urban area customers faced network problems because of poor coverage.

• The small cell enhancement is the only option for better services in future to provide a better quality of services to the Users.

Problem Definition

Objectives

• To set up a platform for performing the simulations. The platform could be chosen from Windows and various Linux flavors such as Fedora.

• To install and set up appropriate software such as NS2, Trace graph, Xgraph, NAM (Network Animator) on the selected platform.

• Develop Compression and Aggregation Algorithm.

• To evaluate the performance of chosen protocol based on their performance metrics such as average delay, Energy, Throughput, and Spectral Efficiency in the simulated environment of AODV(Ad hoc on Demand Distance Vector). • To conclude the results and suggest the future work related to the performance of the protocols.

Lampel Ziv Markov Chain Algorithm

Data Compression and Aggregation

Data is a combination of alphanumeric characters. Data compression is nothing but the encoding of data.

Data Compression algorithm compresses data file so that it takes less storage space.

In order to store and transmit such a data as it is, requires larger memory and increase bandwidth utilization.

Hence, before storage or transmission the size of data has to be reduced without affecting the information content of the data.

Among the various encoding algorithms, the Lempel Ziv Markov Chain Algorithm (LZMA) to be effective in unknown byte stream compression for reliable lossless data compression.

Lossless compression technique is free from loss of data. Using this technique original message can be exactly decoded.

Lossless data compression works by finding repeated patterns in a message and encoding those patterns in an efficient manner.

Jacob Ziv and Abraham Lempel (LZMA) drew attention towards dictionary-based methods to achieve better compression ratios.

The first simple compression algorithm described by Ziv and Lempel is commonly referred to as LZ77.

LZMA Data Compression Algorithm

In LZMA all data follow path : • Address to already coded contents • Sequence length • First deviating symbol

If no identical byte sequence is available from former contents, the address is 0, the sequence length is 0 and the new symbol will be coded.

LZMA Coding Scheme

• LZMA uses a dynamic dictionary to compress unknown data with the use of sliding window algorithm

• Delta Filter and Range Encoder in addition to improve the compression technique.

Delta Encoding and Decoding / Delta Filter

The Delta Filter shapes the input data stream for effective compression by the sliding window.

It stores or transmits data in the form of differences between sequential data rather than complete files.

The output of the first byte delta encoding is the data stream itself.

The subsequent bytes are stored as the difference between the current and its previous byte.

For a continuously varying real time data, delta encoding makes the sliding dictionary more efficient.

Sliding Dictionary

There are two types of dictionaries namely static dictionary and adaptive dictionary. In static dictionaries the entries are predefined and constant according to the application of the text.

In adaptive dictionaries, the entries are taken from the text itself and created on-the-fly.

A search buffer is used as dictionary , Patterns in text are assumed to occur within range of the search buffer.

Use of suitable data structure for the buffers will reduce the search time for longest matches.

Sliding Dictionary encoding is more difficult than decoding as it needs to find the longest match.

Range Encoder

Range encoder encodes all the symbols of the message into a single number to achieve greater compression ratios.

The range encoder uses the following steps. 1) Provide a large-enough range of integers, and probability estimation for the

symbols. 2) Divide the initial range into sub-ranges whose sizes are proportional to the

probability of the symbol they represent. 3) Encode each symbol of the message by reducing the current range down to just that

sub-range which corresponds to the next symbol to be encoded.

The decoder must have the same probability estimation the encoder used, which can either be sent in advance, derived from already transferred data.

Performance Metrics

Delay

• The average time taken by a data packet to arrive in the destination ∑ ( arrive time – send time ) / ∑ Number of connections.

• The average time from the beginning of a packet transmission at a source node until packet delivery to a destination

Energy

• The average energy consumed by the nodes in receiving & sending the packets.

• It includes energy spent in channel listening & packet transmission forwarding in the whole network.

• It is measured in joule.

• Energy permit the network to work.

Throughput

• The system throughput is the sum of data rates that are delivered to all terminals in the network.

• It is the ratio of the number of packet received successfully & the total number of packets transmitted.

• Measured in bits/sec or some time data packets/sec i.e.(p/s or pps)

Project Simulation

• The simulation experiment is carried out in LINUX (FEDORA 7).

• The detailed simulation model is based on Network Simulator-2 (ver-2.34) , is used in the evaluation.

• The NS instructions can be used to define the topology structure of the network and the motion mode of the nodes, to configure the service source and the receiver, to create the statistical data track file and so on.

Development of Data Aggregation and Compression Technique for Communication in Femtocell Network.

Simulator NS-2.34

Protocol AODV

Simulation duration 300 Seconds

Simulation area 300 m x 300 m

Number of nodes 30

Transmission range 1.5 Km

Movement model Radio propagation Model

MAC Layer Protocol IEEE 802.11

Pause Time 0.0001sec

Maximum speed 1000m/s

Packet rate 100 p\sec

Traffic type CBR

Datapay load 100 bits

Parameter values for Simulation

Screenshot showing use of AWK Command

Step 1 : Command for Data Compression and Aggregation

• Here we provide commands to the system for operation

• “0” represents we don’t select Compression & Aggregation Operation

Step 2 : Comparing Every Two Access Point Nodes to Obtain Maximum Energy at Nodes

• We compare every two nodes for detecting maximum energy

• Each node have it’s own energy

• Node which have maximum energy always initiating for data transmission or receiving.

Step 3 : Enter the Source and Destination

• Source node 3

• Destination node 18

• Both Source and Destination Nodes have minimum energy compared to Node 2 and Node 19.

Step 4 : Data Transfer From Source to Destination

• Source Node -3

• But the Data Transfer from Node 3 to Node 2

• Because we know that, Node 2 has maximum energy than Node 3

Step 5 : Data loss between Source and Destination

• At the time of Data transmission from Node 3 to Node 2 the Black Square Box represents the Data loss.

• Data loss is more at the time of Uncompressed Data transmission.

Step 6 : Data transferring Source Node 2 to Femto Node 30

Data Loss is on large scale

Step 7: Data Transfer Femto Node 30 to Node 18 via Node 19

• Data Transfer from Femto Node 30 to Destination Node 18

• But as we know that Node 19 has the maximum energy compared to Node 18 so the Data transfer via Node 19

Step 8: Applying Data Compression and Aggregation Technique

• As select “1” for compression we send command to the system that Data Compression start

Step 9: Data Loss is Minimum in Compressed Data

• Compressed Data Transfer from Node 3 to Node 2

• Data loss is minimum in compressed data compared to the uncompressed data.

Step 10 : Data transmission from Source Node to Femto Node with data minimum loss

• Compressed Data transfer from Source Node 2 to Femto Node 30

• Data loss is also reduce.

Data Lossis minimum

Step 11 : Data transfer from Femto Node 30 to Destination Node 18

Data Loss Reduces

Step 12 : Final Cycle of Compressed Data Transmission from Source Node to Destination Node

• Data transfer from Femto Node 30 to Destination Node 18 via Node 19

• Because the Node19 has maximum energy compared with Node 18

• Data loss is reduces with Compressed Data

RESULT & ANALYSIS

Delay Graph

X axis – time – 0.2 secY axis – delay – 0.2 msec

Delay

Time

Uncompressed Data

Compressed Data

1

Analysis of Delay Values

Simulation Time Compressed Data Uncompressed Data

0.1 sec 0.9 msec 1.0 msec

0.2 sec 0.2 msec 3.0 msec

0.3 sec 1.8 msec 1.8 msec

0.4 sec 0.8 msec 2.4 msec

0.5 sec 1.3 msec 3.1 msec

0.6 sec 2.1 msec 3.4 msec

0.7 sec 0.7 msec 3.2 msec

0.8 sec 1.5 msec 2.0 msec

0.9 sec 0.2 msec 3.4 msec

1.0 sec 0.1 msec 0.6 msec

Sr. No.

1

2

3

4

5

6

7

8

9

10

Delay for Compressed Data

Delay for Uncompressed Data

<

• According to parameter Delay , Compressed data transmission rate is fluent than Uncompressed Data

Energy Graph

Y axis – energy – 0.05 mJX axis – time – 0.2 Sec

Energy

Time

Uncompressed Data

Compressed Data

Analysis of Energy Values

Sr. no. Simulation Time Compressed Data Uncompressed Data

1 0.1sec 1.98 mJ 2 mJ

2 0.2 sec 1.93 mJ 1.97 mJ

3 0.3 sec 1.87 mJ 1.9 mJ

4 0.4 sec 1.84 mJ 1.87 mJ

5 0.5 sec 1.80 mJ 1.83 mJ

6 0.6 sec 1.78 mJ 1.79 mJ

7 0.7 sec 1.73 mJ 1.75 mJ

8 0.8 sec 1.68 mJ 1.78 mJ

9 0.9 sec 1.65 mJ 1.68 mJ

10 1.0 sec 1.7 mJ 1.68 mJ

Energy required for Compressed Data

Energy required for Uncompressed Data

<

• According to the parameter Energy Consumption, Compressed Data consume less energy compared to the Uncompressed Data

Throughput Graph

Bits

Time

Uncompressed Data

Compressed Data

X axis - Time – 0.2 sec Y axis – Bits – 20 bits

Throughput Values

Sr. No. Simulation Time Compressed Data

Uncompressed Data

1 0.2 sec 210 bits/sec 210 bits/sec

2 0.4 sec 230 bits/sec 230 bits/sec

3 0.6 sec 230 bits/sec 230bits/sec

4 0.8 sec 200 bits/sec 210 bits/sec

5 1.0 sec 200 bits/sec 200 bits/sec

• The values of Throughput is nearly same for Compressed & Uncompressed Data.

Spectral Efficiency

For Uncompressed Data

For Compressed Data• Compressed Data achieve improved Spectrum Efficiency

Analysis

• Compressed Data perform much better than Uncompressed Data on performance metrics likes Delay, required Energy, and Spectral Efficiency.

• The Throughput of Compressed and Uncompressed Data is nearly same.

Conclusion

• The Compression and Aggregation technique tone down data loss very accurately.

• Improvement in Femtocell Based Handoff, Operational Time decreases, and also improvement in Energy Efficiency.

• We obtain improved Spectral Efficiency in Femtocell Network.

Future Scope• In the future, simulations could be carried out using project codes, in order to gain a more in-depth performance analysis of the Data Compression Technique in Femtocell Network. • Our work can be extended by evaluating & comparing to various other technique in Femtocell like Self-Organizing Network.

• Also, we may proceed hardware implementation for efficient Femtocell devices in a real world.

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[27] Ian Sharp, Kegen Yu. Senior Member, IEEE, “Enhanced Least- Squares Positioning Algorithm for Indoor Positioning”, IEEE Transactions on Mobile Computing, Vol. 12 No.8, August 2013.[28] Christos Bouras, Georgios Kavourgias, Vasileios Kokkinos, Andres Ppazois, Computre Technology Institute & Press “Diophantus” and Computer Engineering and Informatics Department, University of Patras, Greece. “Interference Management in LTE Femtocell System Using an Adaptive Frequency Reuse Scheme” IEEE 2012.[29] M.Jada, J. Hamalaimen, R. Jantti. Department of Communications and Networking Aalto University, School of Science and Technology Espoo, Finland. “Energy Savings in Mobile Networks : Case Study on Femtocells”. IEEE2014.[30] Atta ul Quddus, Tao Guo, Mehrdad Shariat, Bernard Hunt, Ali Imran, Youngwook Ko., Rahim Tafazoli. Centre for Commmunnication System Research, University of Survey Guildford, .UK. “Next Generation Femtocells : An Enabler for High Efficiency Multimedia Transmission”, IEEE COSMOS MMTC E- Letter, Vol. 5, N0. 5, September 2010.[31] Hakyung Jung, Ji Hoon Lee, Chulhyum Park, Youngbin Im, Taekyong Kwon, Yanghee, Choi, Schoole of Computer Science and Engineering, Seoul National University, Seoul, Korea. “A Femtoell – Based Testbed for Evaluating Futur Cellular Networks”, IEEE 2013.[32] Prof. R.K. Jain, Sumit Katiyar, Research Scholar, Singhania University, Jhunjhunu, India. Prof. N. K. Agrawal Sr. M IEEE Indraprastha Engineering College, Ghaziabad, India. “Hierarchical Cellular Structures in High – Capacity Cellular Communications Systems”, International Journal of Advanced Computer Science and Applications”, Vol.2, N0.9, 2011.[33] Ayaskant Rath, Sha Hua, Shivenda S. Panwar , Dept. of Electrical and Computer Engineering, Polytechnic Institute of NYU, Brooklyn, NY,11201. “FemtoHaul : Using Femtocells with Relays to Increase Macrocell Backhaul Bandwidth” IEEE INFOCOM 2010.

Sr. No. Paper Title Conference Name Journal Publication

Impact Factor

1 Review On : Design of Efficient Femtocell by Lampel Ziv Markov Chain Algorithm.

4th International Conference on Emerging Trends & Research in Engineering, Technology and Science, IBSS College of Engineering, Amravati.

International Journal of Pure and Applied Research in Engineering and Technology, IJPRET, Vol.4 (9) : 579-583, 2016

4.226

2 Design of Efficient Femtocell using LZMA Data Compression Technique.

International Conference on Science & Technology for Sustainable Development , ICS&TSD 2016, Jhulelal Institute of Technology, Nagpur.

International Journal on Recent and Innovation Trends in Communication , IJRITCC, Vol.4, Issue 5, May 2016

5.0998

Designed by : Virendra Uppalwar