A comparison between binary and continuous genetic algorithm for collaborative spectrum optimization in cognitive radio network

The main obstacle for a cognitive radio (CR) is to detect the presence of primary users (PUs) reliably in order to reduce the interference to licensed communications. Genetic algorithms (GAs) are well suited for CR optimization problems to improve spectrum utilization by manipulating its unused portions and offer a solution to the apparent spectrum underutilization problem. In this paper, we present binary genetic algorithm (BGA) and continuous genetic algorithm (CGA)-based soft decision fusion (SDF) scheme for cooperative spectrum optimization in cognitive radio network (CRN). Then BGA-based optimization engine is implemented at the fusion center (FC) of a linear SDF scheme to optimize the linear coefficient vector with other conventional methods. The comparison between BGA and CGA shows that BGA performs better than CGA. Then the results and analysis confirm that BGA is efficient and stable and it outperforms conventional natural deflection coefficient- (NDC), modified deflection coefficient- (MDC-), maximal ratio combining- (MRC-) and equal gain combining- (EGC-) based SDF schemes as well as the OR-rule based hard decision fusion (HDF) scheme. It also verifies that the computation complexity of the BGA method meets the real time requirements of cognitive radio spectrum sensing.