Multiobjective memetic optimization for spectrum sensing and power allocation in cognitive wireless networks

Abstract

The paper presents a multiobjective memetic optimization algorithm for a joint spectrum sensing and power allocation problem in a multichannel, multiple-user cognitive wireless networks. In particular, we apply a multiobjective memetic algorithm to design efficient spectrum sensing and power allocation techniques to maximize the throughputs and minimize the interferences of the network. To maximize the throughputs of secondary users and minimize the interferences to primary users, it requires for a joint determination of the sensing and transmission parameters of the secondary users, such as sensing times, decision threshold vectors, and power allocation vectors. There is a conflict between these two objectives, thus a multiobjective optimization problem is introduced. The proposed algorithm evolutionarily learns to find optimal spectrum sensing times, decision threshold vectors, and power allocation vectors to maximize the averaged opportunistic throughput and minimize the averaged interference (or maximize the averaged transmission gain) of the cognitive network.