Cognitive decision engine based on binary chaotic particle swarm optimization

To solve the problem of transmitter parameter optimization in different communication modes for cognitive radio (CR) systems, a cognitive decision engine based on binary chaotic particle swarm optimization (BCPSO) is proposed. The BCPSO algorithm has both the fast convergence of particle swarm optimization and global ergodic property of chaos. Therefore, the cognitive decision engine based on BCPSO can jump off the local extreme points effectively, which can improve the precision and stability of parameter optimization. The cognitive orthogonal frequency division multiplexing (OFDM) system is used for the performance analysis. And the simulation results show that the proposed cognitive decision engine, which has higher fitness value and stronger robustness for different communication modes, is better than the other existing engines. The proposed engine achieves the objective of parameter optimization effectively.