Feature Selectin Based Spectrum Decision Making for Cognitive Radio Networks

8/3/2019 Feature Selectin Based Spectrum Decision Making for Cognitive Radio Networks

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Feature Selection Based Spectrum Decision Making For

Cognitive Radio Networks

Ms.V.T.Sundaramaheswari1

, Ms.R.Ganga2

P.G Student1

, Assistant professor2, Department of Electronics and Communication

Engineering,Kalasalingam University

Anand Nagar, KrishnanKoil-626196, INDIAE-mail: tsund ar amaheswar [email protected] m

1,

gangae c@red iffmail.co m2

Abstract The growing success of new wireless applications

and services has led to overcrowded licensed bands. Cognitive

radio networks have been proposed as a solution to both

spectrum inefficiency and spectrum scarcity problems by

improving the utilization of limited radio resources. Cognitive

radio networks are intelligent networks that can automatically

sense the environment and adapt the communication parameters

accordingly. These types of networks have applications in

dynamic spectrum access, coexistence of different wireless

networks, interference management etc. A spectrum decision

framework is proposed to determine a set of spectrum bands by

considering the application requirements as well as the dynamic

nature of the spectrum bands. The use of cyclic feature based

methods for distributed signal detection and classification

is discussed and recent results are presented. Moreover, a

dynamic resource management scheme is developed to coordinate

the spectrum decision adaptively dependent on the time- varying

cognitive radio network capacity. Simulation results can be

obtained by using NS2 which shows that the proposed method

provide efficient bandwidth utilization while satisfying service

requirements.

Index Terms Cognitive radio networks, spectrum decision, spectrum characterization, Cyclic feature detection, resource

management.

1.INTRODUCTION

The wireless communication systems are making the

transition from wireless telephony to interactive internet dataand multi-media type of applications, for desired higher

data rate transmission. As more and more devices gowireless, it is not hard to imagine that future technologies

will face spectral crowding, and coexistence of wirelessdevices will be a major issue. Considering the limited

bandwidth availability, accommodating the demand for highercapacity and data rates is a challenging task, requiring

innovative technologies that can offer new ways of exploiting

the available radio spectrum. Cognitive radio is the excitingtechnologies that offer new approaches to the spectrum usage.

Cognitive radio is a novel concept for future wireless

communications, and it has been gaining significantinterest among the academia, industry, and regulatory

bodies. Cognitive Radio provides a tempting solution tospectral crowding problem by introducing the opportunistic

usage of frequency bands that are not heavily occupied by

their licensed users. Cognitive radio concept proposes to

furnish the radio systems with the abilities to measure and

be aware of parameters related to the radio channelcharacteristics, availability of spectrum and power,

interference and noise temperature, available networks, nodes,

and infrastructures, as well as local policies and otheroperating restrictions.The key enabling technology for dynamic spectrum access

techniques is the cognitive radio technology, which providesthe capability to share the wireless channel with licensed users

(or primary users) in an opportunistic manner [1]. Cognitiveradio (CR) networks are envisioned to provide high bandwidth

to mobile users via heterogeneous wireless architectures and

dynamic spectrum access techniques. CR networks, however,

impose unique challenges because of the high fluctuation inthe available spectrum as well as the diverse quality-of-service

(QoS) requirements of various applications. To address thesechallenges, first, CR networks are required to determine which

portions of the spectrum are available, called spectrum sensing[2], [6]. Furthermore, how to coordinate multiple CR users to

share the spectrum band, called spectrum sharing, is anotherimportant issue in CR networks [5], [9]. Although all these

efforts enable CR users to exploit spectrum opportunities

effectively, the heterogenous spectrum environmentintroduces a new critical issue in CR networks. Generally,

CR networks have multiple available spectrum bands over a

wide frequency range that show different channelcharacteristics, and need to support applications withdiverse service requirements. Therefore, once availablespectrum bands are identified through spectrum sensing, CR

networks need to select the proper spectrum bands according

to the application requirements. This process is referred toas spectrum decision.In this paper, an adaptive spectrum decision framework is

proposed with the consideration of all decision events and

application types. First, a novel capacity model is developed to

describe unique characteristics in CR networks byconsidering PU activity as well as sensing capability. The

use of cyclic feature based methods for distributed signaldetection and classification is discussed and recent results are

presented. The decision schemes are controlled by a proposedresource management based on the current network condition.
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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The paper is organized as follows: Section 2 presents related

work. Section 3 describes the system model for spectrumdecision making. In Section 4, a dynamic resource

management scheme is developed. Simulation results are

presented in Section 5. Finally, conclusions are presented inSection 6.

2. RELATED WORK

In a mobile network users are constantly moving and thenetwork topology changes. A topology optimized allocation

algorithm is used in general. It begins with no prior

information. Using this approach networks need to completely

recomputed spectrum assignment for all users after each

change resulting in high computational complexity. Local

bargaining approach is implemented [3] where users affected

by mobility event self organize into bargaining groups and

adapt their spectrum assignment. Without being restricted to

any prefixed spectrum bands, nodes choose operating

spectrum on demand. For the resource-constrained networkssuch as sensor and ad hoc networks, a rule-based spectrum

management is proposed, where CR users access the spectrumindependently according to both local observation and

predetermined rules [4]. In [9], a game theoretic framework is

proposed to analyze the behavior of cognitive radios for

distributed adaptive channel allocation. Two different

objective functions are defined for the spectrum sharing

games, which capture the utility of selfish users and

cooperative users, respectively.

Power allocation among CR users competing the same

spectrum is another important issue in spectrum sharing. In

[7], an optimal power allocation scheme is proposed to

achieve ergodic and outage capacity of the fading channel

under different types of power constraints and fading

range. CR users perform the observations and analysis on

radio environments and feed them to the central base-station,

which decides on spectrum availability and spectrumallocation. Each CR user has multiple software-defined radio

(SDR) transceivers to exploit multiple spectrum bands over awide frequency range by reconfiguring the operating

frequency through software operations.A frequency division duplex (FDD) system is assumed

where uplink and downlink channels are separated. Thus, the proposed decision scheme can be applied to each link

independently.When primary users appear in the spectrum band, CR users

need to move to a new available band, resulting in a temporary

communication break. To solve this problem, multiple

noncontiguous spectrum bands are assumed that can besimultaneously used for the transmission in the CR network.

This method can create a signal that is not only capable of highdata throughput, but is also immune to the PU activity. Even if

a primary user appears in one of the current spectrum bands,the rest of them will maintain current transmissions. The

control channel plays an important role in exchanging

information regarding sensing and resource allocation.In cognitive radio (CR) networks, unused spectrum

bands will be spread over a wide frequency rangeincluding both unlicensed and licensed bands. These

unused spectrum bands detected through spectrum sensing

show different characteristics according to the radioenvironment. Since CR networks canhave multiple

available spectrum bands having different channelcharacteristics, they should be capable of selecting the proper

spectrum bands according to the application requirements,called spectrum decision.

Cognitive

implementationRESOURCE MANAGER

SPE CTRUM

DECISION

models. The fundamental challenge is to ensure the quality of

service (QoS) of the PUs as well as to maximize the

throughput or ensure the QoS, such as signal-to-interference-

plus-noise ratios (SINRs), of the secondary users (SUs). single

New CRuser

Admi ssioncontrol

Decisioncontrol

Feature

selection

input multiple output multiple access channel (SIMO-MAC)

based CR network co-existing with PUs is studied [11]. Two

optimization problems for the SU involving a joint beam

forming and power allocation for the CR network is

considered. The wireless users transmit delay sensitive

multimedia applications over cognitive radio networks.

Delays

Eventdetection

CRtransmi ssion

Spe ctrumsharing

Channel fading

Qualityvar iation

are important for multimedia users due to their delay-

sensitivity nature. The main challenge is to coordinate thespectrum sharing among heterogeneous multimedia users in a

decentralized manner. A dynamic channel selection solution is

proposed [10] for autonomous wireless users transmitting

delay sensitive multimedia applications over cognitive radio

networks.

3. SYSTEM MODEL

An infrastructure-based CR network is considered that has acentralized network entity, such as a base-station. The base-

station exerts control over all CR users within its transmission

Fig 1: The proposed spectrum decision framework.

At first, each spectrum band is characterized for the

spectrum decision, based on not only local observations of CRusers but also statistical information of primary networks.

Through the local measurement, CR users can estimate thechannel conditions such as capacity, bit error rate (BER), and

delay. In order to describe the dynamic nature of CRnetworks, a new metric, primary user activity, defined as the

probability of the primary user appearance during the CR usertransmission. After the spectrum characterization, the CR

network chooses the best spectrum bands.


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3.1 SIGNAL CLASSIFICATION USING

CYCLOSTATIONARY

Once cyclostationary feature detection is applied, certainfeatures are extracted from the primary users waveform

for the purpose of classification of waveform. The two

obvious features are operating frequency and modulation typeof each waveform. In addition to operating frequency and

modulation type, data rate of each waveform can also be

determined.

Feature extraction based on spectral correlation

Cyclostationarity properties of modulated signals were firstderived by Gardner in the middle 90's and they are commonlyused to extract time and frequency domain features that areused to classify the signals among a given set possiblemodulations. Time domain analysis comprises the cyclicautocorrelation function. For a deterministic time series x(t),we define the cyclic autocorrelation function as

(1)The series is said to be wide-sense cyclostationary with period

T0 if is not identically zero for = nT0 for some integers

n, but is identically zero for all other values of .

The Fourier transform of this signal is the so-called spectralcorrelation function, and it can be expressed as

(2)

where is the Fourier transform of the expression , this

is, (3)where Y(t,f) is the Fourier transfor of the signal y(t) in the

intervalThe spectral correlation function can easily be particularized

to a finite interval in order to make it usable in practice. Inauthors also use the so called spectral autocorrelationcoefficient between frequency components placed at a distance

, which is expressed as

(4)

Four characteristics are used and other works to classify

modulations using machine learning algorithms. The first oneconsists of the count of narrow pulses in the frequency domain present in the spectral autocorrelation function. To find this

feature, it is enough to set = 0 and count the number of peaksin the resulting function. The second feature to extract is the

number of spectral lines in the $\alpha$ domain of the spectral

autocorrelation function. The third feature is the average

energy of these pulses. The fourth feature of the set is themaximum value of the spectral correlation coefficient.

4. DYNAMIC RESOURCE MANAGEMENT FOR

SPECTRUM DECISION

Because of the PU activities, available spectrum bands showtime-varying characteristics in the CR network. Thus, with the

only proposed decision schemes, the CR network is not ableto exploit spectrum resources efficiently, and hence results

in the violation of the guaranteed service quality. As a result,the CR network necessitates an additional resource

management scheme to coordinate the proposed spectrumdecision methods adaptively with bandwidth fluctuations. The

main objectives of the proposed resource management are as

follows:

The CR network is capable of determining theacceptance of a new incoming CR user without

any effect on the service quality of currentlytransmitting users.

During the transmission, the CR network needs tomaintain the service quality of currently transmit

ting users by considering the fluctuation of theavailable bandwidth

Since real-time users usually have a higher

priority in spectrum access, best-effort users maynot have enough resources. Thus, the CRnetwork may be required to balance the

bandwidth between both applications.In the following sections, network states are defined to

describe the current spectrum utilization. Based on thesestates, admission control scheme and decision control methods

are proposed.

Spectrum States for Resource Management

To exploit spectrum resources efficiently, the

proposed spectrum decision needs to adapt to the time-

varying network conditions. Thus, the network conditions are

classified into three states according to the bandwidth

utilization. Let W R be the bandwidth currently assigned to

real-time users, and Wav be the total available bandwidth

not occupied by primary users. W min represents the

minimum bandwidth to guarantee the service requirements

of current users. W R, W av , and W min are time-varying

according to the spectrum decision results and PU activities.

The network states are classified as follows:

Underloaded state: If the current occupancy of

real-time users, W R/W av is less than , the CR

network is underloaded. is the predefined

overload threshold to determine if the network is

overloaded or not.

Overloaded state: When W R =W av > , the CRnetwork is now overloaded. remaining bandwidth,

this state can be classified into two substates.

If the expected bandwidth required for the

spectrum decision, W req , is less than the

currently unused bandwidth W av - W R , the CR

network is in the beginning of the overloaded state

and still has enough resources (operating state).

Otherwise, the CR network is almost saturated and

does not have enough bandwidth for the current

spectrum decision operation (saturated state).


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Outage state: If available bandwidth W av is

below W min , the CR network cannot provide the

guaranteed service quality to the currently active

CR users.

If becomes higher, real-time users can have more

stable sustainable rate due to less admission and rate controls,

but the outage probability will be higher.

5. SIMULATION RESULTS

An infrastructure-based CR network consisting of one base

station and multiple CR users. Each user is uniformly

distributed over the network coverage with the radius of 2 km.

The CR network is assumed to operate in 5 licensed spectrum

bands consisting of VHF/UHF TV, WI-FI, GSM, CDMA, and

WEBTV bands. The bandwidth of these bands are 6 MHz

(TV), 5 MHz (WI-FI), 200 kHz (GSM), 1.25 MHz (CDMA),

and 19.4Mbps(HDTV), respectively.

Fig 2 Node deployment

Cognitive MAC implementation

The base station has to detect the presence of primary usersignal. Since most of the time TV channels are underutilized

other secondary users can make use of the channels by sensingthe surrounding environment.

Fig 3 Cognitive Mac implementation

6.CONCLUSION AND FUTURE WORK

Conclusion

A framework for spectrum decision is introduced todetermine a set of spectrum bands by considering the channel

dynamics in the CR network as well as application

requirements. A novel spectrum capacity model is proposedthat considers unique features in CR networks.Cyclostationary feature detection gives better results compared

to Energy detection method at low Signal to Noise Ratios(SNRs). With Cyclostationary spectrum

sensing, the primary usersmodulation scheme can also be easily found out. Moreover, a

dynamic resource management scheme is introduced to enablethe CR network to coordinate spectrum decision adaptively

dependent on the time-varying spectrum resources

Future work

The architecture model is completed and nodes aredeployed. In future cyclostationary feature detection algorithm

will be implemented for the nodes which yields betteraccuracy for spectrum decision making framework. The

performance for the proposed method will be analyzed.

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Feature Selectin Based Spectrum Decision Making for Cognitive Radio Networks

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