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

Abstract

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.