改进加权匹配追踪信道估计算法

吕治国; 齐萌; 邵鸿翔

doi:10.13756/j.gtxyj.2022.01.012

您当前的位置：

首页 >

文章列表页 >

改进加权匹配追踪信道估计算法

无线通信技术 | 更新时间：2022-06-15

- 改进加权匹配追踪信道估计算法
- Improved Weighted Matching Pursuit based Channel Estimation Algorithm
- 光通信研究 2022年第1期页码：63-66,77
- 作者机构：
  
  洛阳理工学院　计算机与信息工程学院，河南　洛阳　471023
- 作者简介：
  
  吕治国(1977-)，男，河南洛阳人。讲师，博士，主要研究方向为移动通信技术。
  齐萌，讲师。E-mail：iemengq@163.com
- 基金信息：
  
  河南省自然科学基金资助项目(202300410286);河南省科技攻关资助项目(192102210249;192102210116;212102210470);河南省高等学校重点资助项目(19A520006;19B510007)
- DOI：10.13756/j.gtxyj.2022.01.012
  中图分类号： TN911
- 纸质出版日期：2022-02-10，
  
  收稿日期：2021-01-03，
扫描看全文
吕治国, 齐萌, 邵鸿翔. 改进加权匹配追踪信道估计算法[J]. 光通信研究, 2022,(1):63-66,77.

Lü Zhi-guo, QI Meng, SHAO Hong-xiang. Improved Weighted Matching Pursuit based Channel Estimation Algorithm[J]. Study on Optical Communications, 2022,(1):63-66,77.
吕治国, 齐萌, 邵鸿翔. 改进加权匹配追踪信道估计算法[J]. 光通信研究, 2022,(1):63-66,77. DOI： 10.13756/j.gtxyj.2022.01.012.

Lü Zhi-guo, QI Meng, SHAO Hong-xiang. Improved Weighted Matching Pursuit based Channel Estimation Algorithm[J]. Study on Optical Communications, 2022,(1):63-66,77. DOI： 10.13756/j.gtxyj.2022.01.012.

摘要

基于压缩感知的匹配追踪算法可以用较短导频序列估计大规模天线通信系统稀疏信道，具有计算复杂度低，需要导频数量少的优点，但信道估计精度不高。依据估计误差大小给各次迭代获得的信道估计值加权，能在低信噪比(SNR)条件下提高估计精度，但会降低高SNR条件下的估计精度。为了解决这个问题，文章提出了一种改进的加权匹配追踪算法。先通过对信道数据训练获取权值信息，然后随迭代次数自适应调整权值，从而改善估计精度性能。仿真结果表明，改进算法在保证低SNR估计精度前提下，改善了高SNR条件下的估计精度性能。

Abstract

Compressive sensing based matching pursuit algorithm can estimate the channel state information of communication system with shorter pilot sequences. It has the advantages of lower computational complexity and less number of pilots. However

the estimation accuracy is relatively low. Assigning different weights to the estimated channel according to the estimation error can improve the estimation performance under low Signal Noise Ratio (SNR) conditions. Nevertheless

it will reduce the estimation accuracy under high SNR conditions. To address this issue

an improved weighted matching pursuit algorithm is proposed. The information of weight value is obtained by training the channel data. Adjusting the weight values adaptively with the iterations can further improve the estimation accuracy. The simulation results show that the proposed algorithm can improve the estimation performance under both low and high SNR conditions.

关键词

大规模天线压缩感知匹配追踪稀疏重建信道估计

Keywords

massive antennacompressed sensingmatching pursuitsparse reconstructionchannel estimation

references

Chen Z, Sohrabi F, Yu W. Multibi估计精Sparse Activity Detection for Massive Random Access: Massive MIMO Versus Cooperative MIMO[J]. IEEE Transactions on Wireless Communications, 2019, 18(8): 4060-4074.

孙泽宇, 吕治国. 大规模MIMO加权正交匹配跟踪信号估计算法[J]. 计算机工程与应用, 2020, 56(5): 118-124.

Liu Y, Zhao M, Xiao L, et al. Pilot Domain NOMA for Grantn应用信号Massive Random Access in Massive MIMO Marine Communication System[J]. China Communications, 2020, 17(6): 131-144.

Figueiredo F A P D, Cardoso F A C M, Moerman I, et al. Channel Estimation for Massive MIMO TDD Systems Assuming Pilot Contamination and Frequency Selective Fading[J]. IEEE Access, 2017, 5: 17733-17741.

Nguyen H. Low Complexity Maxplex Uplink Power Control in Massive MIMO System with Experiment on Testbed[C]//2019 26th International Conference on Telecommunications (ICT). Hanoi, Vietnam: IEEE, 2019: 351-355.

Albreem M A M, Elsaleh A A, Juntti M. Linear Massive MIMO Uplink Detector based on Joint Jacobi and Gaussiorunic Methods[C]//2020 16th International Conference on the Design of Reliable Communication Networks DRCN 2020. Milano, Italy: IEEE, 2020: 1570610672.

Mezghani A, Swindlehurst A L. Blind Estimation of Sparse Broadband Massive MIMO Channels with Ideal and Onealel ADCs[J]. IEEE Transactions on Signal Processing, 2018, 66(11): 2972-2983.

Ali A, Carvalho E D, Health R W. Linear Receivers in Noneiversgnsnt Massive MIMO Channels with Visibility Regions[J]. IEEE Wireless Communications Letters, 2019, 8(3): 885-888.

吴君钦, 李宁, 王加莉. 基于匹配追踪算法阈值改进的MIMO追踪算法阈信道估计研究[J]. 现代电子技术, 2018, 41(1): 9-12, 16.

刘盼盼, 李雷, 王浩宇. 压缩感知中基于变尺度法的贪婪重构算法的研究[J]. 通信学报, 2014, 35(12): 98-105, 115.

朱星, 李有明, 刘小青. 基于压缩感知的脉冲超宽带信号检测[J]. 微波学报, 2014, 30(5): 76-81.

余翔, 郑寒冰, 曾银强. 基于压缩感知的自适应加权匹配追踪算法[J]. 重庆邮电大学学报: 自然科学版, 2016, 28(5): 707-712.

Narayanan S, Sahoo S K, Makur A. Greedy Pursuits based Gradual Weighting Strategy for Weighted l1-Minimization[C]//2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Calgary, AB, Canada: IEEE, 2018: 4664-4668.

Lu W, Wang Y, Hua X, et al. Compressive Downlink Channel Estimation for FDD Massive MIMO using Weighted lp Minimization[C]//2018 IEEE 4th International Conference on Computer and Communications (ICCC). Chengdu, China: IEEE, 2018: 8780953.

Gilabert P L, L lab-Bueno D, Montoro G. Spectral Weighting Orthogonal Matching Pursuit Algorithm for Enhanced Out-of-Band Digital Predistortion Linearization[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2019, 66,(7): 1277-1281.

Papazafeiopoulos A, Kourtessis P, Renzo M D, et al. Performance Analysis of Cell-free Massive MIMO Systems: A Stochastic Geometry Approach[J]. IEEE Transactions on Vehicular Technology, 2020, 69(4): 3523-3537.

更多指标>

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于二进制编码多导频搜索的导频设计

基于压缩感知的OFDM系统信道估计方法

MIMO系统中基于贝叶斯的稀疏信道估计算法

LTE-A基于压缩感知的信道估计

电力线载波通信中基于深度学习的信道估计