Spatial Wideband Channel Estimation for MmWave Massive MIMO Systems with Hybrid Architectures and Low-Resolution ADCs

TL;DR

This paper introduces a novel gridless compressed sensing-based channel estimator for wideband mmWave massive MIMO systems with hybrid architectures and low-resolution ADCs, accounting for spatial wideband effects.

Contribution

It develops a discrete-time channel model for spatial wideband effects and proposes a NFCFGS-CV algorithm with CV-based termination for improved channel estimation accuracy.

Findings

NFCFGS-CV outperforms existing on-grid CS estimators.

The proposed method effectively estimates channels with low-resolution ADCs.

The CV-based termination minimizes squared error.

Abstract

In this paper, a channel estimator for wideband millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with hybrid architectures and low-resolution analog-to-digital converters (ADCs) is proposed. To account for the propagation delay across the antenna array, which cannot be neglected in wideband mmWave massive MIMO systems, the discrete time channel that models the spatial wideband effect is developed. Also, the training signal design that addresses inter-frame, inter-user, and inter-symbol interferences is investigated when the spatial wideband effect is not negligible. To estimate the channel parameters over the continuum based on the maximum a posteriori (MAP) criterion, the Newtonized fully corrective forward greedy selection-cross validation-based (NFCFGS-CV-based) channel estimator is proposed. NFCFGS-CV is a gridless compressed sensing (CS) algorithm, whose termination condition is determined by the CV technique. The CV-based termination condition is proved to achieve the minimum squared error (SE). The simulation results show that NFCFGS-CV outperforms state-of-the-art on-grid CS-based channel estimators.