# Multi-agent Reinforcement Learning for Energy Saving in Multi-Cell Massive MIMO Systems

arXiv: https://arxiv.org/abs/2402.03204 · HTML: https://mlsanity.com/paper/2402.03204/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 4 of 4)

- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Google Research Football: A Novel Reinforcement Learning Environment](https://mlsanity.com/paper/1907.11180/) (2019)
- [Digital Twin Assisted Risk-Aware Sleep Mode Management Using Deep Q-Networks](https://mlsanity.com/paper/2208.14380/) (2022)
- [Energy-Efficient Load-Adaptive Massive MIMO](https://mlsanity.com/paper/1510.00649/) (2015)

## Built on by (most cited first, 2 of 2)

- [Energy Saving for Cell-Free Massive MIMO Networks: A Multi-Agent Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2604.07133/) (2026)
- [Temporally Consistent Graph Q-Networks for Intelligent Network Control](https://mlsanity.com/paper/2606.13848/) (2026)

## Abstract (arXiv)

We develop a multi-agent reinforcement learning (MARL) algorithm to minimize the total energy consumption of multiple massive MIMO (multiple-input multiple-output) base stations (BSs) in a multi-cell network while preserving the overall quality-of-service (QoS) by making decisions on the multi-level advanced sleep modes (ASMs) and antenna switching of these BSs. The problem is modeled as a decentralized partially observable Markov decision process (DEC-POMDP) to enable collaboration between individual BSs, which is necessary to tackle inter-cell interference. A multi-agent proximal policy optimization (MAPPO) algorithm is designed to learn a collaborative BS control policy. To enhance its scalability, a modified version called MAPPO-neighbor policy is further proposed. Simulation results demonstrate that the trained MAPPO agent achieves better performance compared to baseline policies. Specifically, compared to the auto sleep mode 1 (symbol-level sleeping) algorithm, the MAPPO-neighbor policy reduces power consumption by approximately 8.7% during low-traffic hours and improves energy efficiency by approximately 19% during high-traffic hours, respectively.