Channel Estimation for One-Bit Massive MIMO Systems Exploiting Spatio-Temporal Correlations

TL;DR

This paper proposes a novel channel estimation method for one-bit massive MIMO systems that leverages spatio-temporal correlations to improve accuracy despite low-resolution ADCs.

Contribution

It introduces a Bussgang-based linearization combined with Kalman filtering to exploit spatial and temporal correlations for enhanced channel estimation in one-bit MIMO systems.

Findings

Significant improvement in channel estimation accuracy.

Effective utilization of spatio-temporal correlations.

Robust performance with low-resolution ADCs.

Abstract

Massive multiple-input multiple-output (MIMO) can improve the overall system performance significantly. Massive MIMO systems, however, may require a large number of radio frequency (RF) chains that could cause high cost and power consumption issues. One of promising approaches to resolve these issues is using low-resolution analog-to-digital converters (ADCs) at base stations. Channel estimation becomes a difficult task by using low-resolution ADCs though. This paper addresses the channel estimation problem for massive MIMO systems using one-bit ADCs when the channels are spatially and temporally correlated. Based on the Bussgang decomposition, which reformulates a non-linear one-bit quantization to a statistically equivalent linear operator, the Kalman filter is used to estimate the spatially and temporally correlated channel by assuming the quantized noise follows a Gaussian distribution. Numerical results show that the proposed technique can improve the channel estimation quality significantly by properly exploiting the spatial and temporal correlations of channels.