通信学报2025，Vol.46Issue(5)：77-90,14.DOI:10.11959/j.issn.1000-436x.2025093
基于深度残差定点网络的太赫兹UM-MIMO系统信道估计算法
THz UM-MIMO system channel estimation algorithm based on deep residual block fixed-point network
摘要
Abstract
To mitigate the channel estimation challenges induced by hybrid near-far field and beam squint effects in THz ultra-massive MIMO systems,a deep learning-based FPN-OAMP-SRLG algorithm was proposed.A feature extraction network SRLG was constructed by developing a deep residual block(BSRB)with coordinate attention and partial chan-nel shift,along with a gated linear self-attention module(SARG).The channel estimation problem was formulated as an image restoration task through integration with the FPN-OAMP framework.The algorithm utilized pilot information,es-timated via the least squares method,as input features and recovered channel state information through iterative linear and nonlinear estimators.Simulation results demonstrate that the proposed algorithm achieves high-precision THz chan-nel estimation,exhibiting fast convergence and robust performance.关键词
信道估计/THz超大规模MIMO系统/深度残差块/注意力机制Key words
channel estimation/THz ultra-massive MIMO system/deep residual block/attention mechanism分类
电子信息工程引用本文复制引用
于舒娟,魏玉尧,蔡良隆,卢宏宇,张昀,赵生妹..基于深度残差定点网络的太赫兹UM-MIMO系统信道估计算法[J].通信学报,2025,46(5):77-90,14.基金项目
国家自然科学基金资助项目(No.62375140)Foundation Item:The National Natural Science Foundation of China(No.62375140) （No.62375140）
