北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2016, Vol. 39 ›› Issue (3): 22-26.doi: 10.13190/j.jbupt.2016.03.003

• 论文 • 上一篇    下一篇

基于粒子群优化的频域多信道干扰对齐算法

邹卫霞, 王多万, 杜光龙   

  1. 1. 北京邮电大学 泛网无线通信教育部重点实验室, 北京 100876;
    2. 东南大学 毫米波国家重点实验室, 南京 210096
  • 收稿日期:2015-12-24 出版日期:2016-06-28 发布日期:2016-06-27
  • 作者简介:邹卫霞(1972-),女,副教授,E-mail:zwx0218@bupt.edu.cn.
  • 基金资助:

    国家高技术研究发展计划(863计划)项目(2015AA01A703);毫米波国家重点实验室开放课题经费项目(K201501)

On Particle Swarm Optimization for Multi-Frequency Channel Interference Alignment

ZOU Wei-xia, WANG Duo-wan, DU Guang-long   

  1. 1. Key Laboratory of Universal Wireless Communications(Beijing University of Posts and Telecommunications), Ministry of Education, Beijing 100876, China;
    2. State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China
  • Received:2015-12-24 Online:2016-06-28 Published:2016-06-27

摘要:

针对频域干扰对齐系统解空间的多峰值特性,提出了一种基于粒子群优化,以系统网络和速率为优化目标函数的干扰对齐全局搜索算法.该算法通过对速度向量在位置向量的法平面上做投影以加强全局搜索能力,并在粒子群标准位置更新的基础上增加沿目标函数梯度方向的学习搜索来提高算法收敛速度和趋向全局最优值的能力.数值仿真结果表明,该算法可以获得比现有算法更好的网络和速率性能.

关键词: 干扰对齐, 频域多信道, 自由度, 粒子群优化, 梯度, 网络和速率

Abstract:

For multi-frequency channel interference alignment(IA) system with single data stream transmitting for each user,the user solutions of capacities are important. However, there exist methods which can obtain optimal IA solution. Considering the complex multimodal characteristics of solution space of multi-frequency channel in LA system, a new gradient-exploited particle swarm optimization algorithm was proposed to search for the global optimal solution which directly takes the network sum rate as its objective function. The capability for searching the optimal solution is enhanced by projecting the velocity vector on the normal plane of the position vector; the convergence rate is speeded through learning along the gradient of the network sum rate function. Numerical simulation shows that, the proposed method will obtain a better network sum rate performance than that of the existing algorithms.

Key words: interference alignment, multi-frequency channels, degrees of freedom, particle swarm optimization, gradient, network sum rate

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