# Energy Saving for Cell-Free Massive MIMO Networks: A Multi-Agent Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2604.07133 · HTML: https://mlsanity.com/paper/2604.07133/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Builds on (most cited first, 11 of 11)

- [Foundations of User-Centric Cell-Free Massive MIMO](https://mlsanity.com/paper/2108.02541/) (2021)
- [Ubiquitous Cell-Free Massive MIMO Communications](https://mlsanity.com/paper/1804.03421/) (2018)
- [Cell-Free Massive MIMO in O-RAN: Energy-Aware Joint Orchestration of Cloud, Fronthaul, and Radio Resources](https://mlsanity.com/paper/2301.06166/) (2023)
- [Local Partial Zero-Forcing Precoding for Cell-Free Massive MIMO](https://mlsanity.com/paper/1909.01034/) (2019)
- [Kairos: Energy-Efficient Radio Unit Control for O-RAN via Advanced Sleep Modes](https://mlsanity.com/paper/2501.15853/) (2025)
- [Multi-agent Reinforcement Learning for Energy Saving in Multi-Cell Massive MIMO Systems](https://mlsanity.com/paper/2402.03204/) (2024)
- [Optimal Design of Energy-Efficient Cell-Free Massive MIMO: Joint Power Allocation and Load Balancing](https://mlsanity.com/paper/1911.11375/) (2019)
- [Energy efficient cell-free massive MIMO on 5G deployments: sleep modes strategies and user stream management](https://mlsanity.com/paper/2306.06404/) (2023)
- [Energy Reduction in Cell-Free Massive MIMO through Fine-Grained Resource Management](https://mlsanity.com/paper/2405.07013/) (2024)
- [Joint Precoding and AP Selection for Energy Efficient RIS-aided Cell-Free Massive MIMO Using Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2411.11070/) (2024)
- [Energy-Efficient Cell-Free Massive MIMO with Wireless Fronthaul](https://mlsanity.com/paper/2412.02771/) (2024)

## Abstract (arXiv)

This paper focuses on energy savings in downlink operation of cell-free massive MIMO (CF mMIMO) networks under dynamic traffic conditions. We propose a multi-agent deep reinforcement learning (MADRL) algorithm that enables each access point (AP) to autonomously control antenna re-configuration and advanced sleep mode (ASM) selection. After the training process, the proposed framework operates in a fully distributed manner, eliminating the need for centralized control and allowing each AP to dynamically adjust to real-time traffic fluctuations. Simulation results show that the proposed algorithm reduces power consumption (PC) by 56.23% compared to systems without any energy-saving scheme and by 30.12% relative to a non-learning mechanism that only utilizes the lightest sleep mode, with only a slight increase in drop ratio. Moreover, compared to the widely used deep Q-network (DQN) algorithm, it achieves a similar PC level but with a significantly lower drop ratio.