Advanced Coverage Optimization Techniques for Small Cell Clusters

作     者：HUANG Liang ZHOU Yiqing WANG Yuanyuan HAN Xue SHI Jinglin CHEN Xunxun 

作者机构：National Computer Network Emergency Response Technical Team Coordination Center of China Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2015年第12卷第8期

页      面：111-122页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 

基　　金：supported by the National High-Tech Development 863 Program of China (Grant DOS. 2012AA012801) National Natural Science Foundation of China(No.61331009) 

主　　题：small cell cluster coverage op- timization particle swarm optimization gametheory 

摘      要：Coverage challenge for small considered to be a optlmlzation is a main cell clusters which are promising solution to provide seamless cellular coverage for large indoor or outdoor areas. This paper focuses on small cell cluster coverage problems and proposes both centralized and distributed self-optimization methods. Modified Particle swarm optimization （MPSO） is introduced to centralized optimization which employs particle swarm optimization （PSO） and introduces a heuristic power control scheme to accelerate the algorithm to search tbr the global optimum solution. Distributed coverage optimization is modeled as a non-cooperative game, with a utility function considering both throughput and interference. An iterative power control algorithm is then proposed using game theory （DGT） which converges to Nash Equilibrium （NE）. Simulation results show that both MPSO and DGT have excellent performance in coverage optimization and outperform optimization using simulated annealing algorithm （SA）, reaching higher coverage ratio and throughput while with less iterations.