Efficient Nonlinear Precoding for Massive MU-MIMO Downlink Systems with 1-Bit DACs

TL;DR

This paper introduces a highly-efficient nonlinear precoding algorithm for massive MU-MIMO systems with 1-bit DACs, directly solving the nonconvex problem and achieving state-of-the-art accuracy with significantly improved speed.

Contribution

It develops a novel nonlinear precoding algorithm based on the ADMM framework that directly tackles the nonconvex problem, unlike traditional convex relaxation methods.

Findings

Achieves state-of-the-art accuracy comparable to SDR algorithms.

More than 300 times faster than existing SDR-based precoding methods.

Guaranteed to globally converge under mild conditions.

Abstract

The power consumption of digital-to-analog converters (DACs) constitutes a significant proportion of the total power consumption in a massive multiuser multiple-input multiple-output (MU-MIMO) base station (BS). Using 1-bit DACs can significantly reduce the power consumption. This paper addresses the precoding problem for the massive narrow-band MU-MIMO downlink system equipped with 1-bit DACs at each BS. In such a system, the precoding problem plays a central role as the precoded symbols are affected by extra distortion introduced by 1-bit DACs. In this paper, we develop a highly-efficient nonlinear precoding algorithm based on the alternative direction method framework. Unlike the classic algorithms, such as the semidefinite relaxation (SDR) and squared-infinity norm Douglas-Rachford splitting (SQUID) algorithms, which solve convex relaxed versions of the original precoding problem, the new algorithm solves the original nonconvex problem directly. The new algorithm is guaranteed to globally converge under some mild conditions. A sufficient condition for its convergence has been derived. Experimental results in various conditions demonstrated that, the new algorithm can achieve state-of-the-art accuracy comparable to the SDR algorithm, while being much more efficient (more than 300 times faster than the SDR algorithm).