Optimization of Cooperative spectrum Sensing in cognitive radio

Abstract

quotCognitive radio (CR) is a new paradigm in the field of wireless communication system for efficient utilization of radio frequency (RF) spectrum. The Cooperative Spectrum sensing is the key component of cognitive radio technology in which the sensing information from CR users combines at the Fusion centre (common receiver) by soft combination or conventional hard combination techniques. Soft combination has excellent performance but, it requires a lot of overhead. In contrast, the conventional hard combination scheme requires only one bit of overhead, but it has worst performance because of the loss of sensing information. In this research work, use of teaching learning based optimization (TLBO) algorithm as a significant method is proposed to optimize the weighting coefficients vector of observed energy level of sensing information. The TLBO technique evaluate optimal weighting coefficient vector so that the detection probability P_d is improved for given false alarm rate P_f under the Neyman-Pearson criteria and minimize overall probability of sensing error P_e under the Mini-Max criteria. The performance of the proposed TLBO based cooperative spectrum sensing framework is extensively analysed and compared with conventional HDF and SDF based CSS schemes as well as other optimization techniques i.e. particle swarm optimization (PSO) and genetic algorithm (GA) based CSS through simulations. Simulation result shows that performance of TLBO based method is better than conventional HDF scheme i.e. AND, OR, MAJORITY etc. and close to conventional SDF scheme i.e. EGC with low overhead in various fading channel like AWGN, Rayleigh and Nakagami. Proposed TLBO based CSS scheme is effective and stable and also shows better convergence output which confirms the computation complexity is lower compared to GA and PSO based method. newlinequot newline newline