Bayesian based Sparse Channel Estimation Algorithm in MIMO Systems

HOU Xiao-jing; LüZhi-guo; SUN Ze-yu; XING Xiao-fei

doi:10.13756/j.gtxyj.2017.04.17

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Bayesian based Sparse Channel Estimation Algorithm in MIMO Systems

- Bayesian based Sparse Channel Estimation Algorithm in MIMO Systems
- Study on Optical communication Issue 4, Pages: 62-65(2017)
- 作者机构：
  
  1. 洛阳理工学院计算机与信息工程学院
  2. 西安电子科技大学综合业务网理论及关键技术国家重点实验室
  3. 广州大学计算机科学与教育软件学院
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2017.04.17
  CLC： TN919.3
- Published：2017，
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[1]侯小静,吕治国,孙泽宇,邢萧飞.MIMO系统中基于贝叶斯的稀疏信道估计算法[J].光通信研究,2017(04):62-65.

HOU Xiao-jing, LüZhi-guo, SUN Ze-yu, et al. Bayesian based Sparse Channel Estimation Algorithm in MIMO Systems. [J]. (4):62-65(2017)
[1]侯小静,吕治国,孙泽宇,邢萧飞.MIMO系统中基于贝叶斯的稀疏信道估计算法[J].光通信研究,2017(04):62-65. DOI： 10.13756/j.gtxyj.2017.04.17.

HOU Xiao-jing, LüZhi-guo, SUN Ze-yu, et al. Bayesian based Sparse Channel Estimation Algorithm in MIMO Systems. [J]. (4):62-65(2017) DOI： 10.13756/j.gtxyj.2017.04.17.

摘要

针对未知支持集贝叶斯匹配追踪算法中支持集搜索过于复杂的问题

提出一种改进的贝叶斯稀疏信道估计算法。该算法利用信道的稀疏特性来提高信道估计精度

通过计算信道期望来估计高维稀疏信道。为了提高支持集的搜索准确度

改进算法在支配支持集的搜索过程中引入了维特比路径译码的思想

保留所有发生概率差异低于门限的路径

并对支持集上的信道估计值进行计算

最后计算出信道期望并作为最后的信道估计值。理论分析和仿真结果都表明

改进算法在降低算法复杂度的同时还保持了较高的估计精度。

Abstract

Due to the high complexity of the Support Agnostic Bayesian Matching Pursuit（SABMP）algorithm in the support set search

a modified Bayesian sparse channel estimation algorithm is proposed in this paper.The modified Bayesian algorithm can improve the channel estimation accuracy by exploiting the sparse characteristics of the channel.The high-dimensional sparse channel is estimated by calculating the channel expectation.In order to improve the accuracy of the support set search

the idea of Viterbi path decoding is introduced in the search process of dominating support set.All the paths with probability difference below the threshold are preserved.Then

the channel estimation on the chosen support set is carried out and the channel expectation is calculated as the final channel estimate.Theoretical analysis and simulation results show that the lower computational complexity is maintained and the channel estimation accuracy is kept at the same time.

关键词

贝叶斯估计压缩感知维特比译码贪婪算法稀疏重建

Keywords

Bayesian estimationcompressed sensingViterbi decodinggreedy algorithmsparse reconstruction

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