Finite-Alphabet Precoding for Massive MU-MIMO with Low-resolution DACs

TL;DR

This paper proposes novel algorithms for finite-alphabet precoding in massive MU-MIMO systems with low-resolution DACs, improving performance and reducing complexity compared to existing methods.

Contribution

It introduces two new ADMM-based algorithms, IDE and IDE2, tailored for nonlinear discrete precoding in massive MU-MIMO with finite alphabets, addressing convergence issues.

Findings

IDE achieves excellent performance in precoding quality.

IDE2 offers significantly lower computational complexity.

Proposed algorithms outperform state-of-the-art techniques in simulations.

Abstract

Massive multiuser multiple-input multiple-output (MU-MIMO) systems are expected to be the core technology in fifth-generation wireless systems because they significantly improve spectral efficiency. However, the requirement for a large number of radio frequency (RF) chains results in high hardware costs and power consumption, which obstruct the commercial deployment of massive MIMO systems. A potential solution is to use low-resolution digital-to-analog converters (DAC)/analog-to-digital converters for each antenna and RF chain. However, using low-resolution DACs at the transmit side directly limits the degree of freedom of output signals and thus poses a challenge to the precoding design. In this study, we develop efficient and universal algorithms for a downlink massive MU-MIMO system with finite-alphabet precodings. Our algorithms are developed based on the alternating direction method of multipliers (ADMM) framework. The original ADMM does not converge in a nonlinear discrete optimization problem. The primary cause of this problem is that the alternating (update) directions in ADMM on one side are biased, and those on the other side are unbiased. By making the two updates consistent in an unbiased manner, we develop two algorithms called iterative discrete estimation (IDE) and IDE2: IDE demonstrates excellent performance and IDE2 possesses a significantly low computational complexity. Compared with state-of-the-art techniques, the proposed precoding algorithms present significant advantages in performance and computational complexity.