Efficient Majorization-Minimization-Based Channel Estimation for One-Bit Massive MIMO Systems

Fangqing Liu

doi:10.1109/TWC.2021.3050498

Return

Efficient Majorization-Minimization-Based Channel Estimation for One-Bit Massive MIMO Systems

2021-06-01

PRE

Fangqing Liu

Xiaolei Shang *

Heng Zhu

DOI：10.1109/TWC.2021.3050498

Original

Original request for help

Save

Abstract

En 中文

We consider efficient channel estimation for massive multiple-input multiple-output (MIMO) systems using one-bit analog-to-digital converters (ADCs) with antenna-varying thresholds at the receivers. We introduce a computationally efficient majorization-minimization (MM) based maximum likelihood (ML) channel matrix estimator (referred to as 1bMM-ML), which maximizes the one-bit likelihood function iteratively by solving simple linear least squares problems. Moreover, to take into account the low-rank property of the millimeter-wave (mmWave) massive MIMO channels, we add a nuclear-norm based penalty term to the negative log-likelihood function and solve the resulting problem efficiently using the MM approach (referred to as 1bMM-LR). To further enhance the channel estimation performance, we consider an angular-domain channel model and introduce a hybrid approach (referred to as 1bLR-RELAX), which combines 1bMM-LR with an existing parametric algorithm called 1bRELAX, to recover the angular-domain channel parameters. Numerical examples are provided to demonstrate the effectiveness and efficiency of the proposed channel estimation algorithms.

Keywords:

Massive multiple-input multiple-output (MIMO) communications

millimeter-wave (mmWave)

maximum likelihood (ML) estimation

low-rank

majorization-minimization (MM)

one-bit quantization

antenna-varying thresholds

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.