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

2025-07-30

AI Summary

To address the excessive power consumption caused by high-resolution RF chains in cell-free massive MIMO systems, this paper proposes an energy-efficiency optimization framework based on one-bit quantized precoding for 6G green communications. The method jointly optimizes one-bit DACs at the transmitter and low-resolution ADCs at the receiver, incorporating a dynamic antenna deactivation mechanism that adaptively shuts down redundant antennas based on the structure of symbol vectors, along with regularized zero-forcing precoding under a squared ℓ∞-norm constraint solved via the Douglas–Rachford splitting algorithm. Simulation results demonstrate that the proposed scheme significantly reduces hardware power consumption while achieving higher spectral efficiency for cell-edge users compared to benchmark schemes such as SQUID and RZF, thereby enabling a synergistic improvement in both energy efficiency and communication performance.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Balancing power efficiency and performance in cell-free massive MIMO
• Reducing power consumption with low-resolution ADCs/DACs in MIMO
• Enhancing energy efficiency by dynamically deactivating unnecessary antennas

Innovation

• Green one-bit quantized precoding for cell-free MIMO
• Dynamic antenna deactivation based on symbol vectors
• Outperforms SQUID and RZF in energy efficiency