# Design of Massive MU-MIMO Symbol-Level Precoding with Low-Resolution DACs Via ADMM

IEEE systems journal（2024）

Abstract

In a massive multiuser multiple-input multiple-output (MU-MIMO) downlink communication system, it is observed that the primary power consumption is attributed to high-resolution digital-to-analog converters (DACs). Under the need to build the next generation of low-power mobile communications, we study the low-resolution DACs precoding problem in massive MU-MIMO communication systems. A novel symbol-level precoding technique is developed for the low-resolution $b$ -bit DACs system where $b \ge 2$ . Specifically, we first equivalently reconstruct the low-resolution DACs constraints, and then formulate the low-resolution DACs precoding problem as a directly solvable discrete binary optimization problem. By adding a penalty term to relax the discrete binary constraints, the alternating direction multiplier method can effectively solve the problem. In particular, we use an iterative subprocess that directly projects the low-resolution DACs constraints, avoiding the inversion of the quantized precoding signal in the alternating optimization. Furthermore, based on the matrix theory, the proposed algorithm avoids high-dimensional matrix inversion in each iteration, resulting in a reduction in algorithmic complexity. Simulations evaluate the performance of the proposed low-resolution DACs precoding design for different resolutions, where it is shown that using low-resolution DACs can lead to higher power efficiency. In particular, we focus on setup with low-resolution DACs and shows that compared with existing schemes, the proposed algorithm can achieve a performance gain of approximately $\text{0.5}\,\text{dB}$ in the 3-bit DACs case. We further analyze the convergence and computational complexity of the algorithm and verify it through simulation.

MoreTranslated text

Key words

Alternating direction multiplier method (ADMM),low-resolution DACs,massive MU-MIMO,nonconvex optimization,precoding

AI Read Science

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper

Chat Paper

Summary is being generated by the instructions you defined