waleed session 1presntation

8/3/2019 Waleed Session 1Presntation

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Fully Distributed Cooperative Spectrum Sensing forCognitive Radio Ad Hoc Networks

Waleed Ejaz

[email protected] Network Systems Lab

Sejong UniversityRepublic of Korea

December 19, 2011

Co-authors: Najam ul Hasan and Hyung Seok Kim

Sejong University


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Outline

Abstract

Introduction

Related work System model

Gradient based fully distributed approch

Simulation Results Conclusion

Fully Distributed Cooperative Spectrum Sensing for Cognitive Radio Ad Hoc Networks


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Abstract

Cognitive radios are intelligent with capability of sensing theradio environment

Recently cognitive radios are considered for ad hoc environment. Cognitive radio ad hoc network (CRAHNs) have several unique

features. Ad hoc networks are dynamic in nature and have no central

entity for data fusion. Cognitive radio ad hoc networks needs fully distributed

cooperative spectrum sensing. A fully distributed cooperative spectrum sensing is proposed for

cognitive radio ad hoc networks. The proposed scheme is analyzed from the perspective of reliable

sensing and energy consumption.



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Introduction

Recent measurements shows that 15-85% ofthe allocated spectrum is used sporadically

This scarcity is due to spectrum managementpolicies



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Cognitive Radio Networks

A Cognitive Radio is a radio that can changeits transmitter parameters based on interactionwith the environment in which it operates. (Fed

eral Com Commission05)



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Essential Task

The most essential task of CR is to detect thepresence or absence of PU.

Prime concern of spectrum sensing is about

two things: primary system should not be disturbed by CRU co

mmunication

spectrum holes should be detected efficiently for re

quired throughput and quality of service (QoS)



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Infrastructure based network vs CRAHNs

1) Local

observation

2) Decision at

fusion center

3) Reconfiguration4) Reconfiguration

1) Local

observation

2) Cooperation

3) Decision



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Basic Hypothesis Model

The signal r(t) received by the CR user is

1

0

)()(

)()(

Htnths

Htntr

where n(t) AWGN (Additive White Gaussian Noise)

s(t) Transmitted signal of the primary user

h Amplitude gainof the channel

H0 Null hypothesis No licensed user signal in a certain spectrumband.

H1 Alternative hypothesis There exists some licensed user signal.



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Spectrum Sensing Techniques

Most common transmitter detection techniques are:

Energy Detection

Matched Filter detection

Cyclostationary Feature Detection

Only energy detection is considered as local spectrumsensing technique for implementation for the sake of

simplicity.



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Network of CRUs


7

1

8106

43

2

9

5NodeBidirectional links


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Consensus based scheme

This scheme is based on recent advances in consensus algorithmsthat have taken inspiration from self-organizing behavior of

animal groups such as birds, fish, ants, honey bees and others.

A CRU needs only to set up local interactions without a centralize

d node in cognitive radio ad hoc networks.

Limitation of existing technique is that choice of step size depends on maximum number of neighbors of a node in network.



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Assumptions in proposed scheme

The following are two assumptions for the proposed scheme.

There is full duplex wireless connection between

CRU and each of its desired neighbor and remainsconnected until the energy converges

Network topology is modeled as fixed graph.



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The gradient-based algorithm for this purpose can be stated as:

where xm(i) is the energy received neighbors. The converged valueis then compared with the predetermined threshold . Each CRUgets the final decision locally as

mm

mnnn

ix

ixixixix

)(

)()()()1(

.,0

,,1 *

otherwise

xifDecision



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Algorithm

1: Initially CRUn do2: calculate En3: xn(0)=En4: emit xn(0) to neighbours5: for CRUn do6: while (all xn do not converge to any common value x*)7: ifreceive xmfrom neighbour mthen

8: ifxm xn> Dthen9: Exclude mfrom the list of neighbours10: else11: calculate updated xnusing eq on last slide12: end if13: end if14: end while15: ifxn> then16: PU is present17: else18: PU is absent19: end if20: end for



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Simulation Results (1)



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Simulation Results (2)



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Summary

We proposed a fully distributed and scalable cooperativespectrum sensing scheme for CRAHNS.

CRUs exchange the local sensing information to neighbors andconverge after some iteration without the aid of any centralentity.

The point of convergence is compared with a predeterminedthreshold to decide about the presence or absence of primaryuser.

The major advantage of the proposed scheme is that it does not

require any prior knowledge about the degree of the networkthereby saving energy consumption.

It is shown in results that the proposed scheme can achieve theperformance as good as the consensus-based scheme with lessenergy consumption.



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Acknowledgement



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QUESTIONS


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