performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
TRANSCRIPT
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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.
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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
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
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
Pe Vs SNR Curve
• Similar to BER Vs SNR plot
• Best to have the lowest possible Pe for a low SNR value
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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
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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
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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
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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
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Probability of False Alarm, Qf
Pro
babi
lity
of D
etec
tion,
Qd
Pe = 0
Pe = 0.15
Pe = 0.3
CH BS
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
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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
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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.