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Ebrahim Karami Thursday, February 17, 2011. Cognitive radio to exploit sparsity of the spectrum. Licensed users as primary users (PU). Cognitive radios as secondary users (SU). To take advantage of the spectrum sparsity first the spectrum must be sensed (SS).
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Cognitive radio to exploit sparsity of the spectrum. Licensed users as primary users (PU). Cognitive radios as secondary users (SU). To take advantage of the spectrum sparsity first the spectrum must be sensed (SS). Some SS methods are Energy detection, LRT, match filtering, cyclostationary detection and ... . Energy detection does not need extra information about the PU and therefore it is more popular. Introduction Ebkarami@sce.carleton.ca February 17, 2011
Cluster size Optimization in Cooperative Spectrum Sensing Introduction Probability of detection (PD) and probability of false alarm (PF) are two important parameter to measure the performance of the sensing. The interference induced from the SU on the PU is proportional to the probability of misdetection (1-PD). The throughput of the SU is proportional to 1-PF. Ebkarami@sce.carleton.ca February 17, 2011
Cluster size Optimization in Cooperative Spectrum Sensing Introduction Spectrum sensing can be performed as either distributed or cooperative. Cooperative spectrum sensing improve PF for given PD (and vice versa) but it requires extra bandwidth for negotiation between SUs and cognitive BS. the overhead associated with the communication of initial decisions and the waiting time is rather negligible for small cluster sizes, it becomes more important as the cluster size increases. Consequently there is an optimum cluster size which results in the maximum effective throughput. Ebkarami@sce.carleton.ca February 17, 2011
System Model The distribution of the signal at the output of the energy detector in H0 and H1 Scenarios is centralized and non-centralized Chi-square respectively. Ebkarami@sce.carleton.ca February 17, 2011
Sensing Fusion Conventional sensing fusion methods are the And-rule, the Or-rule and the min M out of N rule. Ebkarami@sce.carleton.ca February 17, 2011
Spectrum sharing using coalition games Dynamic distributed coalition formation protocol for spectrum sharing. Analysis of the proposed protocol in terms of throughput, mean and variance of the required time to reach the grand coalition. Outputs: One conference paper accepted for presentation in ICC2011. One journal paper submitted to IEEE Journal on Selected Areas in Communications, special issue on advances in military networking and communications. Previous Work on CR Ebkarami@sce.carleton.ca February 17, 2011
Current Work on CR Optimization of the cooperative spectrum sensing Cooperation between SUs improves the precision of the sensing but it requires some extra bandwidth for negotiations between members of the cluster. We resolve this problem with cluster size optimization. Decisions received from the other members of the cluster is noisy and sometimes not trustable. A new optimized fusion method is proposed to resolve this problem. Sensing fusion is performed as centralized. A new adaptive distributed cooperative spectrum sensing is proposed to resolve this problem. Ebkarami@sce.carleton.ca February 17, 2011
Throughput versus Cluster Size Assume each SU spends m of each Ts symbols for spectrum sensing. The effective throughput of each SU is Ebkarami@sce.carleton.ca February 17, 2011
Numerical Results Normalized achievable throughput for m=0.05Ts Ebkarami@sce.carleton.ca February 17, 2011
Numerical Results Normalized achievable throughput for m=0.2Ts Ebkarami@sce.carleton.ca February 17, 2011
Any comment? Ebkarami@sce.carleton.ca February 17, 2011
