An Adaptive Multiple Threshold Technicque for Cooperative Spectrum Sensing

In this project, we propose the development of new hybrid techniques for cooperative spectrum sensing (CSS), in cognitive radio oriented wireless networks (CROWN), in which we combine hard and soft decisions. The aim is to reduce the number of feedback bits, or equivalently the required feedback rate, at the cost of a small loss in performance compared to the famous equal gain combining (EGC) based spectrum sensing. The proposed approach relies on fusing information from multiple sensors using particle swarm optimation (PSO) and multiple thresholds. The energies from the fuzzy sensors are combined using PSO then added to the crisp decisions of complementary sensors. The performance of developed system will be measured using the Receiver Operating Characteristics (ROC) and the average number of required feedback bits. We will mathematically analyze the ROC of the proposed techniques and confirm our results using simulations. We will also analyze the amount of feedback bits required and the reduction compared to the EGC. All the analysis and simulations will be done under Additive White Gaussian Noise (AWGN) channels and under Rayleigh channels.