计算机与现代化Issue(5)：50-56,7.DOI:10.3969/j.issn.1006-2475.2026.05.006
面向毫米波大规模MIMO系统的信道估计
Channel Estimation for Millimeter Wave Massive MIMO Systems
廖胡平 1Mohammed TEETI1
作者信息
- 1. 东华理工大学信息工程学院,江西 南昌 330013
- 折叠
摘要
Abstract
Massive multiple-input multiple-output(Massive MIMO)technology significantly enhances spectral efficiency and network capacity through large-scale antenna deployment and is recognized as one of the pivotal technologies for future wireless communications.However,the proliferation of antennas introduces prohibitively high pilot overhead and feedback overhead.To address these challenges,a low-overhead channel estimation algorithm is proposed,targeting the channel sparsity characteris-tics of time-division duplexing(TDD)millimeter wave(MMW)Massive MIMO systems.The algorithm is based on an improved learned approximate message passing(LAMP)neural network enhanced by convolutional neural networks(CNNs),where a re-sidual structure is integrated with the efficient channel attention(ECA)mechanism into the CNN architecture.By leveraging ECA's dynamic weight allocation to amplify critical channel features,the proposed CNN replaces the conventional nonlinear shrinkage function in the iterative unfolding layers of LAMP,thereby achieving adaptive enhancement of nonlinear optimization.Theoretical analysis and simulation results demonstrate that the proposed algorithm reduces the normalized mean square error(NMSE)by an average of 10 dB compared to compressed sensing(CS)algorithms,and outperforms deep learning-based LAMP and GM-LAMP algorithms by approximately 1.8 dB.Furthermore,it effectively reduces system overhead while significantly im-proving the accuracy of channel estimation.关键词
毫米波/大规模MIMO/信道估计/信道状态信息/卷积神经网络Key words
millimeter wave/massive MIMO/channel estimation/channel state information/convolutional neural network分类
信息技术与安全科学引用本文复制引用
廖胡平,Mohammed TEETI..面向毫米波大规模MIMO系统的信道估计[J].计算机与现代化,2026,(5):50-56,7.
