Power Allocation Based on Genetic Simulated Annealing Algorithm in Cognitive Radio Networks

To maximize the utility of cognitive networks, the interference constraints to ensure primary users’ Quality of service (QoS) standards is considered in this paper. The interference temperature model was used to model network interference effects, so that the interference caused by cognitive user’s signal transmission cannot surpass the primary user’s interference temperature limitation. Multi-users’ power allocation was studied based on the analysis of multiple interference temperature limitation of the power model and multi-user access power control. The power allocation issue is converted into a multiconstrained nonlinear programming problem with the interference temperature limitation and then an improved simulated annealing genetic algorithm is proposed to solve this problem. Simulation results show that the improved simulated annealing genetic algorithm has better accuracy and convergence performance.