多用户大规模MIMO-OTFS信道参数估计方法
Channel Parameter Estimation for Multi-user Massive MIMO-OTFS Systems
许魁 1张咪 1夏晓晨 1刘洋 2谢威 1邓诚3
作者信息
- 1. 陆军工程大学通信工程学院,江苏南京 210007
- 2. 92330部队,山东青岛 266102
- 3. 32579部队,广西桂林 541000
- 折叠
摘要
高速移动环境会导致信道的双弥散效应,给无线通信系统带来巨大挑战.正交时频空间(orthog-onal time frequency space,OTFS)调制通过将时-频域的双弥散信道转换为时延-多普勒域的平坦衰落信道,能够有效缓解双弥散信道带来的频率和时间选择性衰落的影响.针对多用户大规模多输入多输出(multi-input multi-output,MIMO)OTFS系统中的信道参数估计问题,通过对多天线信道结构特征进行深入分析,将用户与基站间的信道建模为稀疏结构模型.将大规模MIMO信道划分为多个群组,设计了适用于多用户大规模MIMO-OTFS系统的导频图案,提出了基于群组块共稀疏阈值结构化贝叶斯学习信道估计算法.利用估计得到的信道状态信息设计了分数多普勒频移、到达角度等信道参数估计方法,从而进一步感知用户状态.仿真结果表明,提出的信道参数估计算法具有更高的估计精度和系统频谱效率.
Abstract
High-mobility scenarios are known to pose a significant challenge to wireless communica-tions systems due to the resulting doubly-dispersive wireless channel.The orthogonal time frequency space(OTFS)modulation is a two-dimensional modulation method that maps the transmitted signal to the de-lay-Doppler domain.By converting the doubly-dispersive channel in the time-frequency domain into a flat channel in the delay-Doppler domain,the OTFS modulation can effectively overcome the effects of fre-quency selective fading and time selective fading.To address the channel parameter estimation problem in multi-user massive multi-input multi-output(MIMO)OTFS systems,firstly,through in-depth analysis of the multi-antenna channel structure characteristics,the channel between users and base stations is modeled as a sparse structure model.Afterwards,the massive MIMO channel is divided into multiple groups,and a pilot pattern suitable for multi-user massive MIMO-OTFS systems is designed.A sparse Bayesian learn-ing channel estimation algorithm based on group block structure and common sparse threshold is pro-posed.Finally,with the estimated channel state information,a method for estimating channel parameters such as fractional Doppler and angle of arrival is designed to further perceive the users'states.The simula-tion results show that the proposed channel parameter estimation algorithm outperform the traditional methods in estimation accuracy and system spectral efficiency.
关键词
大规模多输入多输出/正交时频空间调制/信道估计/稀疏贝叶斯学习/信道结构共稀疏Key words
massive multi-input multi-output/orthogonal time frequency space modulation/channel estimation/sparse Bayesian learning/channel structure common sparse引用本文复制引用
基金项目
国家自然科学基金(62071485)
国家自然科学基金(62471488)
国家自然科学基金(62271503)
国家自然科学基金(62171119)
江苏省自然科学基金(BK20231485)
江苏省基础研究计划(BK20192002)
出版年
2024
NETL
NSTL
万方数据