• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2014, Vol. 36 ›› Issue (07): 1316-1323.

• 论文 • 上一篇    下一篇

基于分布估计蜂群算法的多用户检测器

刘婷1,张立毅1,2,张晋斌3   

  1. (1.天津商业大学信息工程学院, 天津 300134;2.天津大学电子信息工程学院,天津 300072;
    3.宜昌测试技术研究所,湖北 宜昌 443003)
  • 收稿日期:2013-04-11 修回日期:2013-05-29 出版日期:2014-07-25 发布日期:2014-07-25
  • 基金资助:

    天津市高等学校科技发展基金计划资助项目(20110709)

A multi-user detector based on the binary artificial
bee colony algorithm with estimation of distribution              

LIU Ting1,ZHANG Liyi1,2,ZHANG Jinbin3     

  1. (1.School of Information Engineering,Tianjin University of Commerce,Tianjin 300134;2.School of Electronic Information Engineering,Tianjin University,Tianjin 300072;3.Yichang Testing Technique Research Institute,Yichang 443003,China)
  • Received:2013-04-11 Revised:2013-05-29 Online:2014-07-25 Published:2014-07-25

摘要:

为了提高二进制人工蜂群算法的全局探索能力,提出一种基于分布估计算法的二进制人工蜂群算法,并应用到最优多用户检测技术中,设计出基于分布估计二进制人工蜂群算法的多用户检测方案。该方案采用直接针对离散域的多维邻域搜索策略,加快了收敛速度,避免了连续域到离散域的转换,同时利用分布估计算法获得的全局统计信息产生候选解,提高了算法性能。仿真结果表明,与传统检测器相比,所设计检测器的收敛速度明显加快,误码率性能和抗远近效应能力显著提高。

关键词: 最优多用户检测, 人工蜂群算法, 二进制人工蜂群算法, 分布估计算法

Abstract:

In order to enhance the global exploration ability of the binary artificial bee colony algorithm, a binary artificial bee colony algorithm based on estimation of distribution is proposed. The proposed algorithm is applied on the optimal multiuser detection technique, and the multiuser detection scheme based on the algorithm is designed. The algorithm directly adopts the multidimensional neighborhood search strategy in the discrete domain, thus quickening the convergence speed and avoiding the conversion from continuous domain to discrete domain. Meanwhile, it makes use of the global statistics information obtained by estimation of distribution to generate the candidate solutions. The simulation results show that, compared with the conventional detectors, the convergence speed of the designed detector is faster, the bit error rate and the nearfar resistance ability is significantly improved.

Key words: optimum multi-user detection;artificial bee colony algorithm;binary artificial bee colony algorithm;estimation of distribution algorithm