Adaptive Discrete Particle Swarm Optimization for Cognitive Radios

The adaptation of transmission parameters is a basic and essential process for efficient communication in Cognitive Radios (CR). However, the dynamic nature of wireless channels and the number of transmission parameters to be optimized in such systems adds more complexity to the adaptation process. The Genetic Algorithm (GA) is a well-known evolutionary algorithm for adaptation and optimization of CR systems. However, the convergence speed in GA is low. Recently, Particle Swarm Optimization (PSO) has been used in the CR systems to reduce the computational cost of GA. In this paper, we propose an Adaptive Discrete PSO (ADPSO) algorithm for adaptation of transmission parameters and achievement of Quality of Service (QoS) requirements of a CR node using multi-objective optimization. Simulation results show that ADPSO has faster convergence speed and high fitness values compared to GA and the conventional PSO.