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Energy Efficient Sleep Mode Optimization in 5G mmWave Networks via Multi Agent Deep Reinforcement Learning

arXiv 2511.22105 ↗First posted 27 Nov 2025cs.LGcited by 0

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Abstract (arXiv)

Dynamic sleep mode optimization (SMO) in millimeter-wave (mmWave) networks is essential for maximizing energy efficiency (EE) under stringent quality-of-service (QoS) constraints. However, existing optimization and reinforcement learning (RL) approaches rely on aggregated, static base station (BS) traffic models that fail to capture non-stationary traffic dynamics and suffer from large state-action spaces, limiting real-world deployment. To address these challenges, this paper proposes a multi-agent deep reinforcement learning (MARL) framework using a Double Deep Q-Network (DDQN), referred to as MARL-DDQN, for adaptive SMO in a 3D urban environment with a time-varying and community-based user equipment (UE) mobility model. Unlike conventional single-agent RL, MARL-DDQN enables scalable, distributed decision-making with minimal signaling overhead. A realistic BS power consumption model and beamforming are integrated to accurately quantify EE, while QoS is defined in terms of throughput. The method adapts SMO policies to maximize EE while mitigating inter-cell interference and ensuring throughput fairness. Simulations show that MARL-DDQN outperforms state-of-the-art strategies, including All On, iterative QoS-aware load-based (IT-QoS-LB), MARL-DDPG, and MARL-PPO, achieving up to 0.60 Mbit/Joule EE, 8.5 Mbps 10th-percentile throughput, and meeting QoS constraints 95% of the time under dynamic scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 37 references that are in the map. First posted on arXiv 27 Nov 2025.

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8 of 37 references.

References · 8

  1. 2015Multi-armed Bandits with Application to 5G Small Cellscited by 18
  2. 2021Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Controlcited by 7
  3. 2018DRAG: Deep Reinforcement Learning Based Base Station Activation in Heterogeneous Networkscited by 6
  4. 2018Load-Based On/Off Scheduling for Energy-Efficient Delay-Tolerant 5G Networkscited by 4
  5. 2015Analysis of Handover Failures in Heterogeneous Networks with Fadingcited by 3
  6. 2021Increasing Energy Efficiency of Massive-MIMO Network via Base Stations Switching using Reinforcement Learning and Radio Environment Mapscited by 3
  7. 2020Base Station and Passive Reflectors Placement for Urban mmWave Networkscited by 2
  8. 2024Energy-Efficient Sleep Mode Optimization of 5G mmWave Networks Using Deep Contextual MABcited by 1

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