Cognitive Radio Engine Model Utilizing Soft Fusion Based Genetic Algorithm for Cooperative Spectrum Optimization

摘要

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 toincrease efficiency of bandwidth utilization by manipulating its unusedportions of the apparent spectrum. In this paper, a binary genetic algorithm(BGA)-based soft fusion (SF) scheme for cooperative spectrum sensing incognitive radio network is proposed to improve detection performance andbandwidth utilization. The BGA-based optimization method is implemented at thefusion centre of a linear SF scheme to optimize the weighting coefficientsvector to maximize global probability of detection performance. Simulationresults and analyses confirm that the proposed scheme meets real timerequirements of cognitive radio spectrum sensing and it outperformsconventional natural deflection coefficient- (NDC-), modified deflectioncoefficient- (MDC-), maximal ratio combining- (MRC-) and equal gain combining-(EGC-) based SDF schemes as well as the OR-rule based hard decision fusion(HDF). The propose BGA scheme also converges fast and achieves the optimumperformance, which means that BGA-based method is efficient and quite stablealso.