mlsanityData to 14 Oct 2026

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

arXiv 2402.03204 ↗First posted 5 Feb 2024cs.IT cs.AI cs.LGcited by 2

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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.

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

4 of 15 references.

References · 4

  1. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  2. 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 260
  3. 2022Digital Twin Assisted Risk-Aware Sleep Mode Management Using Deep Q-Networkscited by 3
  4. 2015Energy-Efficient Load-Adaptive Massive MIMOcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Energy Saving for Cell-Free Massive MIMO Networks: A Multi-Agent Deep Reinforcement Learning Approachcited by 0
  2. 2026Temporally Consistent Graph Q-Networks for Intelligent Network Controlcited by 0

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