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Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns
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Performance optimization of hybrid fusion cluster based cooperative spectrum sensing in cr ns

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This presentation shows performance Optimization of Hybrid Fusion Cluster-based Cooperative Spectrum Sensing in Cognitive Radio Networks. For more details, send an email to ayman.elsaleh@gmail.com

This presentation shows performance Optimization of Hybrid Fusion Cluster-based Cooperative Spectrum Sensing in Cognitive Radio Networks. For more details, send an email to ayman.elsaleh@gmail.com

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  • 1. Performance Optimization of Hybrid Fusion Cluster-based Cooperative Spectrum Sensing in Cognitive Radio Networks Presented by : Name : Thong Wing Yew Student ID : 1061103246 Course : Telecommunications Supervisor : Mr. Ayman Abd El-Saleh Moderator : Mr. Aaras Y. Kraidi 1
  • 2. Presentation Outline Objectives Project Overview & Recap of FYP Part I Performance Criteria Simulation Outcomes for Neyman-Pearson and Minimax Criteria Conclusion Recommendations 2
  • 3. 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. 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. 3
  • 4. ProjectOverview 4
  • 5. Project Overview Performance Optimization of Hybrid Fusion Cluster-based Cooperative Spectrum Sensing in Cognitive Radio Networks• Spectrum Under-utilization Cognitive Radio• Detect the presence of licensed Spectrum SensingPU• Destructive channel effects Cooperative Spectrum Sensing• Data Fusion • Soft Decision Fusion (SDF) Hybrid Fusion Scheme • Hard Decision Fusion (HDF)• Implementing Hybrid Fusion Scheme Cluster-based CSS• Evaluate other schemes andparameters that give the best result Performance Optimization 5
  • 6. Spectrum Sensing Spectrum Underutilization Some portions of the frequency band are unused most of the time  CRHidden Terminal ProblemThe accuracy of spectrumsensing is reduced Cooperative SS
  • 7. Cluster-based Cooperative SpectrumSensing Cluster 1 Cluster 2 Cluster Header (CH) Base StationPrimary User (BS) (PU) Cluster 3 Secondary Users (SU) 7
  • 8. Hard Decision Fusion Vs Soft DecisionFusion Soft Decision Fusion (SDF) 0 = PU absent Hard Decision Fusion (HDF) 1 = PU presentCluster Header Base Station (CH) (BS) Fusion Detection Overhead Traffic Techniques Performance Hard Decision Fusion (HDF) Soft Decision Fusion (SDF) 8
  • 9. Probabilities Definition Pdf β H1 Pcr Pd Energy (T) Pmd Pfa Pd = 1 – Pmd = P ( T > β | H1 )  Desired Pfa = 1 – Pcr = P ( T > β | H0 )  Undesired Pmd = 1 – Pd = P ( T < β | H1 )  Undesired Pcr = 1 – Pfa = P ( T < β | H0 )  Desired 9
  • 10. Performance Criteria Neyman-Pearson Criterion & Minimax Criterion
  • 11. 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
  • 12. Neyman-Pearson Criterion For SDF Pd depends on a fixed value of Pf as well as weighting coefficient, ω
  • 13. Soft Decision Fusion Schemes How to search for the best ω in SDF ?Conventional Schemes Proposed SchemesEqual Gain Combination (EGC) Normal Deflection Coefficient (NDC)Weight assigned to M SUs is equally ∑ H 0 covariance matrix under hypothesis H 0distributed *   −1  ω opt , NDC = ω opt , NDC / || ω opt , NDC ||= ∑ H 0 θ ωi = 1 M where θ i = K PRi | g i | 2 | hi | 2 σ S 2Maximal-Ratio Combining (MRC) Modified Deflection Coefficient (MDC)Weight assigned is dependent on the ∑ H1 covariance matrix under hypothesis H1PU SNR value at the SU *    SNRi ω opt ,MDC = ωopt ,MDC / || ω opt ,MDC ||= ∑ H 1 θ −1 ||ω|| = 1 ωi = SNRT 13
  • 14. The ROC CurveReceiver operating characteristic (ROC) as performance evaluation fordifferent simulations. Po a ilit o D t cio , Q 1 r b b y f ee t n 09 . 08 . d 07 . 06 . 05 . 04 . 03 . 02 . E c ll n x ee t 01 . Go o d W rh s o t le s 0 0 02 . 04 . 06 . 08 . 1 Po a il yo F l eAa m Q r b b it f as l r , f Area of 1 = Perfect Test Area of 0.5 = Worthless Test 14
  • 15. Minimax CriterionAssuming Pf = Pm, where Pm= 1-Pd β Consider Pe = Pf + Pm
  • 16. Pe Vs SNR Curve • Similar to BER Vs SNR plot • Best to have the lowest possible Pe for a low SNR value
  • 17. SimulationOutcomes & Results
  • 18. 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 18
  • 19. Sensed Bandwidth, B 1 0.9 0.8 d 0.7Probability of Detection, Q 0.6 0.5 0.4 0.3 8MHz 0.2 6MHz 0.1 4MHz 2MHz 0 0 0.2 0.4 0.6 0.8 1 Probability of False Alarm, Q f Higher Sensed Bandwidth is preferred but …. K = 2.B.Ts 19
  • 20. Sensing Time, Ts 1 0.9 0.8 d 0.7Probability of Detection, Q 0.6 0.5 0.4 0.3 50us 0.2 25us 0.1 10us 1us 0 0 0.2 0.4 0.6 0.8 1 Probability of False Alarm, Q f Ts Tx Longer Sensing Time is good but …. Access Period 20
  • 21. Number of SU per Cluster, M 1 0.9 0.8Probability of Detection, Q d 0.7 0.6 0.5 0.4 0.3 M=15 0.2 M=10 0.1 M=5 M=1 0 0 0.2 0.4 0.6 0.8 1 Probability of False Alarm Q , f Higher M gives better results! 21
  • 22. Number of Clusters, N 1 P b b i y fDt c o ,Q 0.9 r ai t o e t n 0.8 e i d 0.7 0.6 0.5 l 0.4 o 0.3 N1 =0 N8 = 0.2 N6 = 0.1 N4 = N2 = 0 0 0.2 0.4 0.6 0.8 1 P b bi y fF l e l r ,Q r ai t o a A m o l s a f Higher N gives better results! 22
  • 23. MN Combination (Neyman-Pearson) 1 0.9 0.8 d 0.7Probability of Detection, Q 0.6 0.5 0.4 MN = 15 MN = 4 0.3 M=15, N=1 0.2 M=5, N=3 0.1 M=3, N=5 M=1, N=15 0 0 0.2 0.4 0.6 0.8 1 Probability of False Alarm, Q f When M increases More SDF involved Better Performance 23
  • 24. Probability of Reporting Error, Pe 1 0.99 0.98Probability of Detection, Qd 0.97 0.96 0.95 0.94 0.93 Pe = 0 0.92 P = 0.15 e 0.91 Pe = 0.3 0.9 0 0.2 0.4 0.6 0.8 1 Probability of False Alarm Q , f CH BS
  • 25. Threshold Analysis (SNR=10dB)
  • 26. Threshold Analysis (SNR=5dB)
  • 27. Single Link Sensing Schemes • SDF has better performance than HDF • Proposed SDF schemes are better than conventional SDF schemes
  • 28. Double Link Sensing Schemes (Neyman Pearson) 1 0.9 0.8 0.7 d Probability of Detection, Q 0.6 0.5 0.4 0.3 SDF-SDF(NDC-NDC) SDF-HDF(NDC-OR) 0.2 SDF-SDF(MRC-MRC) SDF-HDF(MRC-OR) 0.1 SDF-SDF(EGC-EGC) SDF-HDF(EGC-OR) HDF-HDF (OR-OR) 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Probability of False Alarm, Q f Spectrum SDF SDF Sensing HDF HDF Primary User (PU) Secondary Users (SU) Cluster Header (CH) Base Station (BS)
  • 29. Double Link Sensing Schemes (Minimax)
  • 30. 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) 30
  • 31. 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. 31
  • 32. Thank You Q & A Session 32

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