Computationally Efficient Maximum Likelihood Channel Estimation for Coarsely Quantized Massive MIMO Systems

被引：10

作者：

Liu, Fangqing ^{[1
]}

Shang, Xiaolei ^{[1
]}

Cheng, Yuanbo ^{[1
]}

Zhang, Guoyang ^{[1
]}

机构：

[1] Univ Sci & Technol China, Dept Elect Engn & Informat Sci, Hefei 230027, Anhui, Peoples R China

来源：

IEEE COMMUNICATIONS LETTERS | 2022年 / 26卷 / 02期

关键词：

Maximum likelihood estimation; Channel estimation; Massive MIMO; Quantization (signal); Signal processing algorithms; Convex functions; Convergence; Low-resolution analog-to-digital converters (ADCs); massive multiple-input multiple-output (MIMO) communications; maximum likelihood (ML) channel estimation; majorization-minimization (MM); ALGORITHM; WIRELESS; UPLINK;

D O I：

10.1109/LCOMM.2021.3133705

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

We consider computationally efficient maximum likelihood (ML) channel estimation for massive multiple-input multiple-output (MIMO) systems using coarsely quantized measurements obtained from low-resolution analog-to-digital converters (ADCs) at the receivers. We first devise a computationally efficient ML estimator (referred to as CQML) by using the cyclic optimization and majorization-minimization (MM) techniques. Then, we show the connections between CQML and the conventional unquantized ML estimator. Numerical examples are provided to demonstrate the effectiveness and computational efficiency of the proposed channel estimation algorithm.

引用

页码：444 / 448

页数：5

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