Development of a cognitive radio decision engine using multi-objective hybrid genetic algorithm

Cognitive radio (CR) is an emerging promising technology for future wireless communication networks. It makes use of intelligent control methods to determine the optimal set of radio transmission parameters for a given status of dynamic wireless channel environment. This paper presents an adaptive CR decision engine driven by a multi-objective hybrid genetic algorithm (HGA) to determine the optimal set of radio transmission parameters for a single carrier system. It has been observed through the performance simulations that the HGA-based CR optimization engine is significantly outperforming the GA-based CR engine in terms of convergence speed and quality of solutions. Thus, this research work exhibits the importance of hybridization in enhancing the processing speed that is of crucial demand in real-time online applications.