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

TL;DR

This paper introduces a binary genetic algorithm-based soft fusion scheme for cooperative spectrum sensing in cognitive radio networks, significantly enhancing detection accuracy and bandwidth efficiency over traditional methods.

Contribution

It proposes a novel BGA-based optimization at the fusion center to improve spectrum sensing performance in cognitive radio, outperforming existing fusion schemes.

Findings

Outperforms conventional fusion schemes like NDC, MDC, MRC, EGC, and OR-rule HDF.

Achieves faster convergence and optimal detection performance.

Meets real-time spectrum sensing requirements.

Abstract

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 increase efficiency of bandwidth utilization by manipulating its unused portions of the apparent spectrum. In this paper, a binary genetic algorithm (BGA)-based soft fusion (SF) scheme for cooperative spectrum sensing in cognitive radio network is proposed to improve detection performance and bandwidth utilization. The BGA-based optimization method is implemented at the fusion centre of a linear SF scheme to optimize the weighting coefficients vector to maximize global probability of detection performance. Simulation results and analyses confirm that the proposed scheme meets real time requirements of cognitive radio spectrum sensing 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). The propose BGA scheme also converges fast and achieves the optimum performance, which means that BGA-based method is efficient and quite stable also.