移动通信2024，Vol.48Issue(7)：40-45,94,7.DOI:10.3969/j.issn.1006-1010.20240524-0001
基于数字孪生信道的大规模MIMO信道低开销预测研究
Low-Overhead Channel Prediction for Massive MIMO Based on Channel Twin
摘要
Abstract
Accurate Channel State Information(CSI)is critical for optimizing the performance of massive Multiple Input Multiple Output(MIMO)systems.However,the large number of antennas in these systems results in significant overhead and latency for CSI acquisition.This paper proposes a novel channel prediction method based on a Crossformer network,leveraging digital twin channels to achieve precise and efficient CSI prediction,thereby guiding the physical channels accurately.The method utilizes historical CSI data from multiple previous time slots and terminal position coordinates in the twin environment as inputs to the neural network.Initially,a dimensional segmentation module embeds the multivariate time series inputs into a two-dimensional vector array to preserve temporal,spatial,and cross-dimensional information.Subsequently,a second-order attention layer captures dependencies across multiple dimensions.Finally,a hierarchical encoder-decoder structure exploits the spatiotemporal dependencies of the channel and cross-dimensional dependencies between the environment and the channel to jointly predict the CSI for multiple future time slots.The proposed channel prediction framework is evaluated against traditional LSTM and Transformer models,demonstrating its superior performance.关键词
大规模MIMO/Crossformer网络/数字孪生信道/依赖关系Key words
Massive MIMO/Crossformer network/digital twin channel/dependencies分类
电子信息工程引用本文复制引用
沈子冰,于力,张建华,张宇翔,张振,王启星,姜涛..基于数字孪生信道的大规模MIMO信道低开销预测研究[J].移动通信,2024,48(7):40-45,94,7.基金项目
国家重点研发计划"面向6G复杂应用场景的高动态无线环境预测与重建"(2023YFB2904803) （2023YFB2904803）
国家自然科学基金"智慧车间复杂传播环境感知、信道重构与资源配置理论研究"(92167202) （92167202）
国家杰出青年科学基金"无线信道的建模理论与实验研究"(61925102) （61925102）
北京邮电大学-中国移动研究院联合创新中心 （）
