Simultaneous Channel Estimation and Localization of Terahertz Massive MIMO Systems via Bayesian Tensor Decomposition

被引:9
作者
Du, Jianhe [1 ]
Dong, Jingyi [1 ]
Gao, Feifei [2 ]
机构
[1] Commun Univ China, State Key Lab Media Convergence & Commun, Beijing 100024, Peoples R China
[2] Tsinghua Univ, Dept Automation, Beijing 100084, Peoples R China
基金
中国国家自然科学基金; 北京市自然科学基金;
关键词
Tensors; Channel estimation; Transmission line matrix methods; Bayes methods; Millimeter wave communication; Massive MIMO; OFDM; Bayesian; tensor; estimation; localization; massive MIMO; OFDM;
D O I
10.1109/LCOMM.2022.3223336
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
In this letter, a Bayesian tensor-based scheme is proposed for simultaneous channel estimation and localization (SCEAL) in wideband terahertz (THz) massive multiple-input multiple-output (MIMO) systems with hybrid analog-digital architectures. Considering the beam squint effect, we construct the received training signal into a sixth-order tensor model, including the angle-of-arrival (AOA)/angle-of-departure (AOD), time delay, and path gains. Then, a real-valued Bayesian inference algorithm is developed to fit the constructed tensor model for SCEAL. The proposed algorithm achieves SCEAL with and without line-of-sight (LOS) path, and offers higher accuracy compared to the state-of-the-art algorithms. Simulations verify the effectiveness of the proposed algorithm.
引用
收藏
页码:541 / 545
页数:5
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