Channel Estimation for Spatially/Temporally Correlated Massive MIMO Systems with One-Bit ADCs

TL;DR

This paper introduces an adaptive channel estimation method for massive MIMO systems with one-bit ADCs, leveraging spatial and temporal correlations to significantly enhance estimation accuracy.

Contribution

It proposes a novel adaptive estimation technique combining Bussgang decomposition and Kalman filtering for correlated channels in one-bit massive MIMO systems.

Findings

Significant improvement in channel estimation accuracy.

Effective use of spatial and temporal correlations.

Low complexity estimator with negligible performance loss.

Abstract

This paper considers the channel estimation problem for massive multiple-input multiple-output (MIMO) systems that use one-bit analog-to-digital converters (ADCs). Previous channel estimation techniques for massive MIMO using one-bit ADCs are all based on single-shot estimation without exploiting the inherent temporal correlation in wireless channels. In this paper, we propose an adaptive channel estimation technique taking the spatial and temporal correlations into account for massive MIMO with one-bit ADCs. We first use the Bussgang decomposition to linearize the one-bit quantized received signals. Then, we adopt the Kalman filter to estimate the spatially and temporally correlated channels. Since the quantization noise is not Gaussian, we assume the effective noise as a Gaussian noise with the same statistics to apply the Kalman filtering. We also implement the truncated polynomial expansion-based low complexity channel estimator with negligible performance loss. Numerical results reveal that the proposed channel estimators can improve the estimation accuracy significantly by using the spatial and temporal correlations of channels.