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Performance Optimization of Hybrid Fusion Cluster-based Cooperative

Spectrum Sensing in Cognitive Radio Networks

Presented by :

Name : Thong Wing YewStudent ID : 1061103246Course : Telecommunications

Supervisor : Mr. Ayman Abd El-SalehModerator : Mr. Aaras Y. Kraidi

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Presentation Outline

Objectives Project Overview & Recap of FYP Part I Performance Criteria Simulation Outcomes for Neyman-Pearson and

Minimax Criteria Conclusion Recommendations

Objectives Part I

Derivation of mathematical model of the soft-hard fusion for cognitive radio network using Neyman-Pearson criterion.

Compare the effects of different channel’s parameters on the performance of the system.

Evaluate the impact of different number of users of the system on the performance of the system.

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Part II Derivation of mathematical model of the soft-hard fusion for cognitive

radio network using Minimax criterion. Evaluation of Threshold Analysis by simulation and mathematical

derivation. Evaluate the similar parameters and effect of users of the system

using the framework of Minimax.

Project Overview

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Performance Optimization of Hybrid Fusion Cluster-based Cooperative Spectrum Sensing in Cognitive Radio Networks

• Spectrum Under-utilization Cognitive Radio

• Detect the presence of licensed PU

Spectrum Sensing

• Destructive channel effects Cooperative Spectrum Sensing

• Data Fusion

• Soft Decision Fusion (SDF)

• Hard Decision Fusion (HDF)Hybrid Fusion Scheme

Cluster-based CSS• Implementing Hybrid Fusion Scheme

• Evaluate other schemes and parameters that give the best result Performance Optimization

Project Overview

Spectrum Sensing

Spectrum Underutilization

Some portions of the frequency band are unused most of the time CR

Hidden Terminal Problem

The accuracy of spectrum sensing is reduced

Cooperative SS

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Cluster-based Cooperative Spectrum Sensing

Primary User (PU)

Secondary Users (SU)

Base Station (BS)

Cluster 1

Cluster 3

Cluster 2

Cluster Header (CH)

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Hard Decision Fusion Vs Soft Decision Fusion

Soft Decision Fusion (SDF)

Cluster Header (CH)

Base Station (BS)

0 = PU absent

1 = PU presentHard Decision Fusion (HDF)

Fusion Techniques

Detection Performance

Overhead Traffic

Hard Decision Fusion (HDF)

Soft Decision Fusion (SDF)

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Probabilities Definition

H1

Pdf

Energy (T)

PdPcr

Pmd Pfa

Pd = 1 – Pmd = P ( T > β | H1 ) Desired

β

Pfa = 1 – Pcr = P ( T > β | H0 ) Undesired

Pcr = 1 – Pfa = P ( T < β | H0 ) Desired

Pmd = 1 – Pd = P ( T < β | H1 ) Undesired

Performance Criteria

Neyman-Pearson Criterion

&

Minimax Criterion

Neyman-Pearson Vs Minimax

Neyman-Pearson Criterion (FYP Part I) Minimal interference caused to PU Maximize Pd for a given Pf

The threshold is fixed

Minimax Criterion (FYP Part II) Higher chances of interfering PU (more aggressive) Minimize the total Pe = Pf + Pm

The threshold is adjusted dynamically

Neyman-Pearson Criterion

Pd depends on a fixed value of Pf

as well as weighting coefficient, ω

For SDF

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Soft Decision Fusion SchemesHow to search for the best ω in SDF ? Conventional Schemes Proposed Schemes

Equal Gain Combination (EGC)Weight assigned to M SUs is equally distributed

Normal Deflection Coefficient (NDC)

covariance matrix under hypothesis H0

Maximal-Ratio Combining (MRC) Weight assigned is dependent on the PU SNR value at the SU

||ω|| = 1

Modified Deflection Coefficient (MDC) covariance matrix under hypothesis H1

Mi1=ω

T

ii SNR

SNR=ω

0H∑

θωωω 1

0,,,* ||||/ −∑== HNDCoptNDCoptNDCopt

1H∑

θωωω 1

1,,,* ||||/ −∑== HMDCoptMDCoptMDCopt

222 |||| SiiRii hgPK σθ =where

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The ROC CurveReceiver operating characteristic (ROC) as performance evaluation for different simulations.

Area of 1 = Perfect TestArea of 0.5 = Worthless Test

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Probability of False Alarm, Qf

Pro

bability of Detection, Q

d

ExcellentGoodWorthless

Minimax Criterion

Consider Pe = Pf + Pm

Assuming Pf = Pm, where Pm= 1-Pd

β

Pe Vs SNR Curve

• Similar to BER Vs SNR plot

• Best to have the lowest possible Pe for a low SNR value

Simulation Outcomes

&

Results

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Parameters To Be Evaluated

Sensing Bandwidth, B Sensing Time of Secondary Users, Ts

Number of SU per Cluster, M Number of Clusters, N Probability of Reporting Error, Pe

Different Combinations of MN Different Spectrum Sensing Schemes Threhold Analysis for Minimax

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Sensed Bandwidth, B

Higher Sensed Bandwidth is preferred but …. K = 2.B.Ts

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Probability of False Alarm, Qf

Pro

babi

lity

of D

etec

tion,

Qd

8MHz6MHz4MHz2MHz

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Sensing Time, Ts

Longer Sensing Time is good but ….

Ts Tx

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Probability of False Alarm, Qf

Pro

babi

lity

of D

etec

tion,

Qd

50us25us10us1us

Access Period

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Number of SU per Cluster, M

Higher M gives better results!

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Probability of False Alarm, Qf

Pro

babi

lity

of D

etec

tion,

Qd

M=15M=10M=5M=1

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Number of Clusters, N

Higher N gives better results!

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Probability of False Alarm, Qf

Probability of Detection, Q

d

N=10N=8N=6N=4N=2

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Probability of False Alarm, Qf

Pro

babi

lity

of D

etec

tion,

Qd

M=15, N=1M=5, N=3M=3, N=5M=1, N=15

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MN Combination (Neyman-Pearson)

MN = 15 MN = 4

When M increases More SDF involved Better Performance

Probability of Reporting Error, Pe

0 0.2 0.4 0.6 0.8 10.9

0.91

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

1

Probability of False Alarm, Qf

Pro

babi

lity

of D

etec

tion,

Qd

Pe = 0

Pe = 0.15

Pe = 0.3

CH BS

Threshold Analysis (SNR=10dB)

Threshold Analysis (SNR=5dB)

Single Link Sensing Schemes

• SDF has better performance than HDF

• Proposed SDF schemes are better than conventional SDF schemes

Double Link Sensing Schemes (Neyman Pearson)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Probability of False Alarm, Qf

Pro

babi

lity

of D

etec

tion,

Qd

SDF-SDF(NDC-NDC)SDF-HDF(NDC-OR)SDF-SDF(MRC-MRC)SDF-HDF(MRC-OR)SDF-SDF(EGC-EGC)SDF-HDF(EGC-OR)HDF-HDF (OR-OR)

Primary User (PU) Secondary Users (SU) Cluster Header (CH) Base Station (BS)

Spectrum Sensing

SDFHDF

SDFHDF

Double Link Sensing Schemes (Minimax)

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Conclusion Cognitive radio is a way to maximize spectrum

utilization

Hard Fusion – Less Overhead but Poorer Performance Soft Fusion - Better Performance but Higher Overhead

Employing Soft-Hard Fusion to get the best of both methods (Hybrid Fusion)

Higher Sensing Time and Bandwidth yields better detection performance

The proposed SDF schemes (NDC & MDC) outperform the conventional SDF ones (EGC & MRC)

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Recommendation for Future Works

• Explore the possibilities and effect of introducing the weighting coefficients at different stages or links of the network.

• Determine the best number of SU per cluster that gives the best detection performance.

• Efficient way of selecting CH, either from an ordinary SU or a dedicated BS.

• Develop an algorithm that minimize the sensing time of a SU.

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Thank You

Q & A Session