Investigation of Channel Estimation Techniques with 1-bit Quantization and Oversampling for Multiple-Antenna Systems

TL;DR

This paper explores channel estimation in large-scale multi-antenna systems using 1-bit quantization and oversampling, proposing a low-resolution aware estimator and analyzing its performance.

Contribution

It introduces a novel linear MMSE channel estimator tailored for 1-bit oversampled systems and derives an analytical performance bound.

Findings

The proposed estimator improves channel estimation accuracy.

Oversampling partially mitigates information loss from 1-bit quantization.

Performance bounds validate the effectiveness of the estimator.

Abstract

Large-scale multiple-antenna systems have been identified as a promising technology for the next generation of wireless systems. However, by scaling up the number of receive antennas the energy consumption will also increase. One possible solution is to use low-resolution analog-to-digital converters at the receiver. This paper considers large-scale multiple-antenna uplink systems with 1-bit analog-to-digital converters on each receive antenna. Since oversampling can partially compensate for the information loss caused by the coarse quantization, the received signals are firstly oversampled by a factor M. We then propose a low-resolution aware linear minimum mean-squared error channel estimator for 1-bit oversampled systems. Moreover, we characterize analytically the performance of the proposed channel estimator by deriving an upper bound on the Bayesian Cram\'er-Rao bound. Numerical results are provided to illustrate the performance of the proposed channel estimator.