Enhanced Tensor-Based Joint Channel and Symbol Estimation in Dual-Hop MIMO Relaying Systems

被引：2
|
作者：
Han, Xi [1 ]
Zhao, Xinyuan [1 ]
de Almeida, Andre L. F. [2 ]
Freitas, Walter da C. [2 ]
Bai, Wenle [1 ]
机构：
[1] North China Univ Technol, Sch Informat Sci & Technol, Beijing 100144, Peoples R China
[2] Univ Fed Ceara, Dept Teleinformat Engn, BR-60020181 Fortaleza, Ceara, Brazil
来源：
IEEE COMMUNICATIONS LETTERS | 2021年 / 25卷 / 05期
基金：
北京市自然科学基金;
关键词：
Channel estimation; Tensors; MIMO communication; Relays; Estimation; Computational modeling; Training; EALS algorithm; channel estimation; dual-hop; PARATUCK2;
D O I：
10.1109/LCOMM.2020.3045109
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, an enhanced alternating least squares (EALS) algorithm for joint channel and symbol estimation is proposed in a dual-hop multiple input multiple output (MIMO) relaying communication system based on a simplified Khatri-Rao space-time (KRST) coding at the source node. The proposed method exploits the parallel factor (PARAFAC) and PARATUCK2 models at the relay node and the destination node, respectively. Exploiting the structure of these tensor models, we present an iterative alternating estimation scheme using optimized step size factors for accelerated convergence and lower overall complexity compared to conventional approaches. Identifiability conditions and computational complexity are analyzed, and simulation results are provided to corroborate the effectiveness of the proposed algorithm.
引用
收藏
页码：1655 / 1659
页数：5
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