Multi-objective Optimization of Spectrum Sensing and Power Allocation Based on Improved Slime Mould Algorithm

Abstract

In order to improve the throughput of sub-users of cognitive radio networks and reduce the interference to primary users, firstly, we consider the spectrum sensing time, decision threshold, and power allocation factors of joint sub-users, and construct the average network throughput and average interference as a multi-objective optimization problem, and secondly, propose a multi-objective optimization algorithm based on improved sticky bacterium algorithm (SMA) to model and solve this problem to obtain the best the sensing time, decision threshold and power. Finally, the proposed method is compared with the NSGA-II algorithm, and the simulation results show that the proposed method can effectively search for the optimal solutions of the spectrum sensing parameters to maximize the average network throughput and minimize the average interference.