An Optimal Bit Allocation Scheme For Cooperative Spectrum Sensing In Cognitive Radio Networks

Hard decision (HD) and soft decision (SD) are two common decision fusion methods used in cooperative spectrum sensing (CSS). In these two fusion methods, the number of bits transmitted by each secondary user to the fusion center is always same and static, namely one bit in HD and n (n >= 2) bits in SD. This paper proposes an optimal bit allocation scheme based on Genetic Algorithm (GA-BAS) for CSS over imperfect channels in cognitive radio networks (CRNs) to minimize energy consumption and maximize the detection probability under the Neyman-Pearson (NP) Criterion at the same time, in which the number of bits transmitted by each secondary user to the fusion center is different. In addition, a novel quantization method called MOE-FAP, which is based on the maximum output entropy (MOE) and satisfies the given local false alarm probability, is proposed for each secondary user (SU). A quantization table can be maintained and held by each SU. To optimize the energy consumption objective and the detection probability objective under the constraint, an improved Genetic Algorithm (IGA) is proposed to allocate the optimal number of bits to each SU. Simulation results show the efficiency and advantages of the proposed scheme, and comparisons with SD, HD and the equal gain combining (EDG) scheme are presented and analyzed.