Energy efficient power allocation in cognitive radio network using coevolution chaotic particle swarm optimization

详细信息    查看全文

• 作者：Meiqin Tang ; ^{meiqintang@aliyun.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Yalin Xin ^{xyl_aya@sohu.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Cognitive radio (CR) network ; Power allocation ; Large scale global optimization ; Coevolutionary chaotic particle swarm optimization (CCPSO)
• 刊名：Computer Networks
• 出版年：2016
• 出版时间：8 May 2016
• 年：2016
• 卷：100
• 期：Complete
• 页码：1-11
• 全文大小：768 K

文摘

In this paper, the trade-off between utility and energy consumption in orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) network is investigated. Energy efficiency problem is very important in the field of CR network, where the utility is maximized and the energy consumption is minimized in such a CR network. Since the trade-off between them has been paying more attentions in literature, this study summarizes the power allocation as an optimization problem that maximizes the energy efficiency via a new energy efficiency metric defined by this paper. The formulated problem is a large-scale nonconvex problem, which is very difficult to solve. In this paper, we present an improved particle swarm optimization (PSO) algorithm to solve the difficult large-scale optimization problem directly. Given the weak convergence of the original PSO around local optima, an improved version that combines the chaos theory is proposed in this study, where chaos theory can help PSO search for solutions around the personal and global bests. In addition, for the purpose of accelerating the convergence process when facing with such a large-scale optimization, the original problem is decomposed into a number of small ones by employing the coevolutionary methodology, and then divide-and-conquer strategy is used to avoid producing infeasible solutions. Simulations demonstrate that the proposed coevolution chaotic PSO needs a smaller number of iterations and can achieve more energy efficiency than the other algorithms.