Abstract
In cooperative spectrum sensing (CSS), there are two ways for the secondary users (SU) to deliver their sensing data or local decisions to the information fusion center (FC): hard-decision (HD) and soft-decision (SD). In HD or SD, the number of bits transmitted by the SUs is always the same and static. However, considering the differences of different SUs in the environment, remaining energy, distances to the FC, and so on, the number of bits transmitted by different SUs should be different. Besides, the reliability of transmitted data by different SUs to the FC is also different. Therefore, this paper proposes an optimized bit allocation scheme based on the genetic algorithm (GA-BAS) for CSS in cognitive radio networks (CRNs). In the proposed scheme, the number of bits transmitted by each SU is different and would be allocated by the FC according to GA-BAS algorithm, and the FC would fuse the transmitted data by each SU with an allocated weight, which could represent the reliability of the SU. Firstly, a simple quantization scheme based on the sub-partitioning of the local decision space is designed to quantify the raw sensing data. Then, the objective function of the overall detection probability and the objective function of energy consumption about the number of allocated bits and the value of the allocated weight of each SU are derived. Finally, the number of allocated bits of each SU would be optimized by an improved genetic algorithm, and an overall decision rule would be given to obtain a global decision. Simulation results show that the proposed scheme (GA-BAS) gets a tradeoff between energy consumption and detection performance. In addition, the proposed algorithm achieves better detection performance, which is close to that of the equal gain combining scheme (EGC), but consumes less energy.
An Optimal Bit Allocation Scheme Based on Genetic Algorithm for Cooperative Spectrum Sensing in Cognitive Radio NetworksAn Optimal Bit Allocation Scheme Based on Genetic Algorithm for Cooperative Spectrum Sensing in Cognitive Radio Networks
