UAV-Assisted Resilience in 6G and Beyond Network Energy Saving: A Multi-Agent DRL Approach
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915918425096192 |
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| author | Dinh, Dao Lan Vy Mai, Anh Nguyen Thi Tran, Hung Vu, Giang Quynh Le Ho, Tu Dac Pan, Zhenni Van, Vo Nhan Chatzinotas, Symeon Tran, Dinh-Hieu |
| author_facet | Dinh, Dao Lan Vy Mai, Anh Nguyen Thi Tran, Hung Vu, Giang Quynh Le Ho, Tu Dac Pan, Zhenni Van, Vo Nhan Chatzinotas, Symeon Tran, Dinh-Hieu |
| contents | 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 may become inactive due to unexpected events such as power outages, disasters, hardware failures, or erroneous energy-saving decisions made by external network management systems. During the time required to reactivate these cells, UAVs are deployed to temporarily restore user service. 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, 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_07366 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UAV-Assisted Resilience in 6G and Beyond Network Energy Saving: A Multi-Agent DRL Approach Dinh, Dao Lan Vy Mai, Anh Nguyen Thi Tran, Hung Vu, Giang Quynh Le Ho, Tu Dac Pan, Zhenni Van, Vo Nhan Chatzinotas, Symeon Tran, Dinh-Hieu Networking and Internet Architecture Machine Learning 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 may become inactive due to unexpected events such as power outages, disasters, hardware failures, or erroneous energy-saving decisions made by external network management systems. During the time required to reactivate these cells, UAVs are deployed to temporarily restore user service. 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, 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. |
| title | UAV-Assisted Resilience in 6G and Beyond Network Energy Saving: A Multi-Agent DRL Approach |
| topic | Networking and Internet Architecture Machine Learning |
| url | https://arxiv.org/abs/2511.07366 |