Green One-Bit Quantized Precoding in Cell-Free Massive MIMO

TL;DR

This paper introduces a novel low-resolution quantized precoding algorithm for cell-free massive MIMO systems that improves energy efficiency by dynamically deactivating antennas, outperforming existing methods in simulations.

Contribution

The paper presents a new quantized precoding algorithm that enhances energy efficiency in cell-free massive MIMO by antenna deactivation based on symbol structure.

Findings

Outperforms SQUID and RZF algorithms in simulations

Reduces power consumption while maintaining high performance

Enhances energy efficiency in massive MIMO systems

Abstract

Cell-free massive MIMO (multiple-input multiple-output) is expected to be one of the key technologies in sixth-generation (6G) and beyond wireless communications, offering enhanced spectral efficiency for cell-edge user equipments by employing joint transmission and reception with a large number of antennas distributed throughout the region. However, high-resolution RF chains associated with these antennas significantly increase power consumption. To address this issue, the use of low-resolution analog-to-digital and digital-to-analog converters (ADCs/DACs) has emerged as a promising approach to balance power efficiency and performance in massive MIMO networks. In this work, we propose a novel quantized precoding algorithm tailored for cell-free massive MIMO systems, where the proposed method dynamically deactivates unnecessary antennas based on the structure of each symbol vector, thereby enhancing energy efficiency. Simulation results demonstrate that our algorithm outperforms existing methods such as squared-infinity norm Douglas-Rachford splitting (SQUID) and regularized zero forcing (RZF), achieving superior performance while effectively reducing power consumption.