频分双工大规模MIMO系统时变信道估计  

Time-Varying Channel Estimation for Frequency-Division Duplex Massive MIMO Systems

作　　者：杜福德[1] 谢威 夏晓晨 Du Fude;Xie Wei;Xia Xiaochen(Unit 65054 of PLA, Dalian, Liaoning 116026, China;Army Engineering University of PLA, Nanjing, Jiangsu 210007, China)

机构地区：[1]中国人民解放军65054部队,辽宁大连116026 [2]中国人民解放军陆军工程大学通信工程学院,江苏南京210007

出　　处：《信号处理》2020年第3期397-406,共10页Journal of Signal Processing

基　　金：国家自然科学基金项目(61901519,61671472);江苏省自然科学基金项目(BK20181335)。

摘　　要：大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统的性能增益依赖可靠的信道估计,传统信道估计方案主要面向准静态场景,在用户高速移动场景中性能下降明显。本文研究频分双工(Frequency Division Duplex,FDD)大规模MIMO系统中的时变信道估计问题,利用信道向量在角度域的空时稀疏特性,提出软结构先验模型驱动的稀疏贝叶斯信道估计(Soft-Structured Prior Model based Sparse Bayesian Estimation,SSPM-SBE)方案,针对方案涉及的复杂贝叶斯估计问题,给出基于变分优化的低复杂度求解方法。SSPM-SBE方案能够充分利用当前和历史接收导频数据改善时变信道的估计性能,且无需信道大尺度信息的先验认知,仿真结果验证了方案的优越性。The gain of massive multiple-input multiple-output(MIMO)systems relies on reliable channel estimation.Most of the traditional channel estimation schemes were designed for quasi-static situations which will suffer from significant performance degradation in scenarios with high user mobility.This paper investigated the time-varying channel estimation problem in frequency division duplex(FDD)massive MIMO systems.A soft-structured prior model based sparse Bayesian estimation(SSPM-SBE)scheme was proposed by exploiting the spatial-temporal property of the angular domain channel.The variational optimization framework was employed to solve the complex Bayesian estimation problem arisen in the proposed scheme.The SSPM-SBE can utilize both the current and previous received pilot signals to improve the estimation performance,and does not require the prior of large-scale channel information.The simulation results demonstrate the superiority of the proposed scheme.

关 键 词：大规模多输入多输出系统 频分双工 时变信道估计 空时稀疏性 

分 类 号：TN929.5[电子电信—通信与信息系统]