UAV-Assisted Resilience in 6G and Beyond Network Energy Saving: A Multi-Agent DRL Approach

2025-11-10

AI Summary

To address coverage holes and degraded network resilience caused by ground base station (GBS) sleep modes or failures in energy-efficient 6G networks, this paper proposes a cooperative optimization framework based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG), jointly optimizing unmanned aerial vehicle (UAV) trajectories, transmit power allocation, and dynamic user-UAV association. Its key innovation lies in modeling UAVs as mobile edge nodes that enable self-healing coverage and energy-efficiency co-optimization under partial GBS deactivation. By leveraging distributed multi-agent reinforcement learning, the framework simultaneously respects UAV endurance constraints and guarantees user quality-of-service (QoS). Experimental results demonstrate that, compared to the full-GSB-activation baseline, the proposed method achieves theoretically optimal coverage, reduces total energy consumption by 24%, and maintains comparable service rates—thereby significantly enhancing the joint optimization of energy efficiency and communication resilience.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Adversarial Agents
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper investigates the unmanned aerial vehicle (UAV)-assisted resilience perspective in the 6G network energy saving (NES) scenario. More specifically, we consider multiple ground base stations (GBSs) and each GBS has three different sectors/cells in the terrestrial networks, and multiple cells are turned off due to NES or incidents, e.g., disasters, hardware failures, or outages. To address this, we propose a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to enable UAV-assisted communication by jointly optimizing UAV trajectories, transmission power, and user-UAV association under a sleeping ground base station (GBS) strategy. This framework aims to ensure the resilience of active users in the network and the long-term operability of UAVs. Specifically, it maximizes service coverage for users during power outages or NES zones, while minimizing the energy consumption of UAVs. Simulation results demonstrate that the proposed MADDPG policy consistently achieves high coverage ratio across different testing episodes, outperforming other baselines. Moreover, the MADDPG framework attains the lowest total energy consumption, with a reduction of approximately 24% compared to the conventional all GBS ON configuration, while maintaining a comparable user service rate. These results confirm the effectiveness of the proposed approach in achieving a superior trade-off between energy efficiency and service performance, supporting the development of sustainable and resilient UAV-assisted cellular networks.

Problem

• Optimizing UAV trajectories and power for network resilience during outages
• Maximizing user coverage while minimizing UAV energy consumption in 6G networks
• Ensuring long-term UAV operability during ground base station sleep modes

Innovation

• Multi-agent DRL optimizes UAV trajectories and power
• UAVs provide coverage when ground base stations sleep
• Framework minimizes UAV energy use while maximizing coverage