pith. sign in

arxiv: 2511.07366 · v3 · submitted 2025-11-10 · 💻 cs.NI · cs.LG

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

Dao Lan Vy Dinh , Anh Nguyen Thi Mai , Hung Tran , Giang Quynh Le Vu , Tu Dac Ho , Zhenni Pan , Vo Nhan Van , Symeon Chatzinotas
show 1 more author
Dinh-Hieu Tran
This is my paper

Pith reviewed 2026-05-17 23:47 UTC · model grok-4.3

classification 💻 cs.NI cs.LG
keywords UAV-assisted resilience6G network energy savingMADDPGmulti-agent DRLcoverage optimizationsleeping base stationstrajectory and power control
0
0 comments X

The pith

UAVs with multi-agent DRL restore coverage in 6G networks when ground base stations fail, using less energy than baselines.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how UAVs can temporarily replace inactive ground base stations in 6G networks during power outages or energy-saving shutdowns. It introduces a multi-agent deep deterministic policy gradient method that coordinates several UAVs to choose their flight paths, transmission power, and user connections at once. The objective is to keep as many users served as possible while using the least UAV battery over time. Simulations indicate this method maintains higher coverage than other approaches and achieves the lowest overall energy draw without hurting service rates much. If the results hold, networks could handle sudden cell failures more gracefully and with better efficiency.

Core claim

The MADDPG framework enables UAVs to jointly optimize trajectories, transmit power, and user associations under a sleeping GBS strategy, maximizing coverage ratio during outages while minimizing long-term UAV energy consumption and preserving comparable user service rates.

What carries the argument

Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework that lets multiple UAV agents learn coordinated policies for trajectory, power, and association control in a shared environment with inactive ground cells.

If this is right

  • High coverage ratio is maintained across repeated test episodes with inactive cells.
  • Total UAV energy consumption is the lowest among the tested methods.
  • User service rate stays comparable to simpler baselines.
  • A practical trade-off between energy efficiency and coverage performance is achieved for resilient 6G operation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same coordination approach could apply to other sudden infrastructure losses such as disaster-damaged towers.
  • Policies trained this way might need periodic retraining when user density or traffic patterns shift over months.
  • Extending the framework to include wind or battery degradation models could make the energy savings more reliable in field use.

Load-bearing premise

The simulation models of UAV movement, wireless channels, and user behavior match real conditions closely enough that the learned policies will perform well and run fast enough when deployed live.

What would settle it

Running the trained policy on physical UAVs in an outdoor test with real user mobility and measuring whether coverage or energy figures match the simulation results within a small margin.

Figures

Figures reproduced from arXiv: 2511.07366 by Anh Nguyen Thi Mai, Dao Lan Vy Dinh, Dinh-Hieu Tran, Giang Quynh Le Vu, Hung Tran, Symeon Chatzinotas, Tu Dac Ho, Vo Nhan Van, Zhenni Pan.

Figure 2
Figure 2. Figure 2: CTDE-MADDPG Framework IV. THE PROPOSED CTDE-MADDPG FRAMEWORK A. CTDE-MADDPG Framework We adopt a Centralized Training with Decentralized Exe￾cution (CTDE) variant of MADDPG for cooperative UAVs control with better memory and training efficiency as shown 2. Each UAV is modeled as an agent with its own actor–critic pair. During training, each agent’s critic has access to the global state sglobal(t) and actio… view at source ↗
Figure 4
Figure 4. Figure 4: omparison of instantaneous reward per step, averaged over 15 testing [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of Total Coverage Ratio per Episode [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
read the original 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 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to deploy UAVs for temporary service restoration in 6G networks when ground base stations (GBSs) become inactive due to power outages, disasters, or energy-saving decisions. The approach jointly optimizes UAV trajectories, transmission power, and user-UAV associations under a sleeping GBS strategy to maximize user coverage while minimizing UAV energy consumption. Simulation results claim that the MADDPG policy achieves consistently high coverage ratios, the lowest total energy consumption, and comparable user service rates relative to baselines across testing episodes.

Significance. If the results hold under realistic conditions, the work could support development of resilient, energy-efficient UAV-assisted 6G networks by addressing trade-offs in coverage and UAV operability during network disruptions. The multi-agent DRL formulation for coordinated UAV control is a relevant technical contribution to UAV-assisted communications.

major comments (2)
  1. [System Model] System model / energy consumption definition: the UAV energy model appears to sum only transmission power (or a linear proxy) without a propulsion term. Since propulsion typically dominates (>90%) real UAV consumption and is velocity/acceleration-dependent, the reported 'lowest total energy consumption' ranking versus baselines is likely an artifact of the incomplete model rather than a genuine optimization outcome. This directly undermines the central trade-off claim in the abstract and results.
  2. [Simulation Results] Simulation setup and evaluation: the environment details (channel models, UAV flight dynamics, user mobility patterns, exact hyperparameter choices for MADDPG, number of testing episodes, and any statistical significance tests) are not described at a level that allows reproduction or rules out post-hoc episode selection. This weakens confidence in the consistent outperformance claims.
minor comments (2)
  1. [Problem Formulation] Notation for multi-agent state/action spaces and reward components could be clarified with explicit equations to improve readability.
  2. [Figures] Figure captions for trajectory and energy plots should include axis units and baseline labels for immediate interpretability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We have carefully reviewed each point and outline our responses below, along with planned revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [System Model] System model / energy consumption definition: the UAV energy model appears to sum only transmission power (or a linear proxy) without a propulsion term. Since propulsion typically dominates (>90%) real UAV consumption and is velocity/acceleration-dependent, the reported 'lowest total energy consumption' ranking versus baselines is likely an artifact of the incomplete model rather than a genuine optimization outcome. This directly undermines the central trade-off claim in the abstract and results.

    Authors: We acknowledge this valid observation. Our current energy model in Section III emphasizes transmission power as the controllable variable tied to the network energy-saving and coverage objectives, treating propulsion as a fixed baseline cost for the short-duration restoration scenario. We agree that a velocity- and acceleration-dependent propulsion term is essential for realism, as it dominates real UAV consumption. In the revised manuscript, we will incorporate a standard UAV propulsion energy model (e.g., based on established aerodynamic formulations), update the total energy objective and reward function, and re-evaluate all results and comparisons. This will directly support and strengthen the trade-off claims in the abstract and results sections. revision: yes

  2. Referee: [Simulation Results] Simulation setup and evaluation: the environment details (channel models, UAV flight dynamics, user mobility patterns, exact hyperparameter choices for MADDPG, number of testing episodes, and any statistical significance tests) are not described at a level that allows reproduction or rules out post-hoc episode selection. This weakens confidence in the consistent outperformance claims.

    Authors: We agree that greater detail is required for reproducibility. In the revised version, we will expand Section IV (Simulation Setup) to explicitly describe the channel models (path-loss exponents, shadowing, and fading parameters), UAV flight dynamics (maximum speed, acceleration limits, and turning constraints), user mobility patterns (static or random waypoint models with specific parameters), exact MADDPG hyperparameters (actor/critic learning rates, discount factor, replay buffer size, exploration noise, and training episodes), the number of testing episodes (we used 1000 episodes averaged over 10 independent runs), and statistical measures (standard deviations, error bars, and significance tests). We will also clarify that all episodes were included without selective reporting. These additions will enable full reproduction and increase confidence in the outperformance results. revision: yes

Circularity Check

0 steps flagged

No circularity: performance claims arise from forward simulation against external baselines

full rationale

The paper defines a MADDPG multi-agent RL framework whose objective (coverage maximization subject to energy minimization) is implemented as a reward function inside a simulated environment. Training produces a policy that is then evaluated in held-out episodes against independent baseline algorithms. No equation reduces the reported coverage ratio or energy value to a fitted parameter or self-referential definition; the numerical outcomes are generated by executing the learned policy in the forward model rather than being algebraically entailed by the inputs. The derivation chain therefore remains self-contained and externally falsifiable.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; typical RL hyperparameters and simulation assumptions are expected but not stated.

pith-pipeline@v0.9.0 · 5603 in / 1042 out tokens · 28067 ms · 2026-05-17T23:47:06.682975+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages

  1. [1]

    Network Energy Saving for 6G and Beyond: A Deep Re- inforcement Learning Approach,

    D. -H. Tran, N. Van Huynh, S. Kaada, V . N. V o, E. Lagunas and S. Chatzinotas, "Network Energy Saving for 6G and Beyond: A Deep Re- inforcement Learning Approach," 2025 IEEE Wireless Communications and Networking Conference (WCNC), Milan, Italy, 2025, pp. 1-6, doi: 10.1109/WCNC61545.2025.10978758

    work page doi:10.1109/wcnc61545.2025.10978758 2025

  2. [2]

    Joint Use of Drone-Mounted Base Stations and Cell Outage Compensation in Emergency Scenarios,

    T. R. Pijnappel, J. L. Van Den Berg, S. C. Borst and R. Litjens, "Joint Use of Drone-Mounted Base Stations and Cell Outage Compensation in Emergency Scenarios," 2024 15th IFIP Wireless and Mobile Networking Conference (WMNC), Venice, Italy, 2024, pp. 1-8, doi: 10.52545/3-1

    work page doi:10.52545/3-1 2024

  3. [3]

    UA V Assisted BS Sleep Strategy for Green Com- munication,

    H. Li et al., "UA V Assisted BS Sleep Strategy for Green Com- munication," in IEEE Transactions on Network Science and En- gineering, vol. 12, no. 5, pp. 3770-3783, Sept.-Oct. 2025, doi: 10.1109/TNSE.2025.3565316

    work page doi:10.1109/tnse.2025.3565316 2025

  4. [4]

    Energy-Efficient UA V Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach,

    C. H. Liu, Z. Chen, J. Tang, J. Xu and C. Piao, "Energy-Efficient UA V Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach," in IEEE Journal on Selected Areas in Communications, vol. 36, no. 9, pp. 2059-2070, Sept. 2018, doi: 10.1109/JSAC.2018.2864373

    work page doi:10.1109/jsac.2018.2864373 2059

  5. [5]

    Wang, Y ., Fang, W., Ding, Y ., & Xiong, N. (2021). Computation offloading optimization for UA V-assisted mobile edge computing: a deep deterministic policy gradient approach. Wireless Networks, 27(4), 2991– 3006

    work page 2021

  6. [6]

    Littman, Anthony R

    Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra, Planning and acting in partially observable stochastic domains, Artificial Intelligence, V olume 101, Issues 1–2, 1998, Pages 99-134, ISSN 0004- 3702

    work page 1998

  7. [7]

    Noise Parameterization of Continuous Deep Reinforcement Learning for a Class of Non-linear System,

    A. Surriani, O. Wahyunggoro and A. I. Cahyadi, "Noise Parameterization of Continuous Deep Reinforcement Learning for a Class of Non-linear System," 2022 14th International Conference on Information Technology and Electrical Engineering (ICITEE), Yogyakarta, Indonesia, 2022, pp. 24-29

    work page 2022

  8. [8]

    A Power Consumption Model and Energy Saving Techniques for 5G-Advanced Base Stations,

    M. Oikonomakou, A. Khlass, D. Laselva, M. Lauridsen, M. Deghel and G. Bhatti, "A Power Consumption Model and Energy Saving Techniques for 5G-Advanced Base Stations," 2023 IEEE International Conference on Communications Workshops (ICC Workshops), Rome, Italy, 2023, pp. 605-610

    work page 2023

  9. [9]

    An Analytical Energy Performance Evaluation Methodology for 5G Base Stations,

    S. K. G. Peesapati, M. Olsson, M. Masoudi, S. Andersson and C. Cavdar, "An Analytical Energy Performance Evaluation Methodology for 5G Base Stations," 2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Bologna, Italy, 2021, pp. 169-174

    work page 2021

  10. [10]

    Satellite- and Cache- Assisted UA V: A Joint Cache Placement, Resource Allocation, and Trajectory Optimization for 6G Aerial Networks,

    D. -H. Tran, S. Chatzinotas and B. Ottersten, "Satellite- and Cache- Assisted UA V: A Joint Cache Placement, Resource Allocation, and Trajectory Optimization for 6G Aerial Networks," in IEEE Open Journal of Vehicular Technology, vol. 3, pp. 40-54, 2022

    work page 2022

  11. [11]

    Throughput Maximization for Backscatter- and Cache-Assisted Wireless Powered UA V Technol- ogy,

    D. -H. Tran, S. Chatzinotas and B. Ottersten, "Throughput Maximization for Backscatter- and Cache-Assisted Wireless Powered UA V Technol- ogy," in IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 5187-5202, May 2022

    work page 2022

  12. [12]

    T., Liu, K

    Chang, W., Meng, Z. T., Liu, K. C., & Wang, L. C. (2021). Energy- Efficient Sleep Strategy for the UBS-Assisted Small-Cell Network. IEEE Transactions on Vehicular Technology, 70(5), 5178-5183. Article 9416880. https://doi.org/10.1109/TVT.2021.3075603

    work page doi:10.1109/tvt.2021.3075603 2021

  13. [13]

    Energy Management in Cellular HetNets Assisted by Solar Powered Drone Small Cells,

    A. Alsharoa, H. Ghazzai, A. Kadri and A. E. Kamal, "Energy Management in Cellular HetNets Assisted by Solar Powered Drone Small Cells," 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA, 2017, pp. 1-6, doi: 10.1109/WCNC.2017.7925568

    work page doi:10.1109/wcnc.2017.7925568 2017

  14. [14]

    Gaddam, Akhileswar Chowdary & Ramamoorthi, Yoghitha & Kumar, Abhinav & Cenkeramaddi, Linga Reddy. (2021). Joint Resource Allo- cation and UA V Scheduling With Ground Radio Station Sleeping. IEEE Access. PP. 1-1. 10.1109/ACCESS.2021.3111087

    work page doi:10.1109/access.2021.3111087 2021

  15. [15]

    ML-Based 5G Traffic Generation for Prac- tical Simulations Using Open Datasets,

    Y . -H. Choi et al., "ML-Based 5G Traffic Generation for Prac- tical Simulations Using Open Datasets," in IEEE Communications Magazine, vol. 61, no. 9, pp. 130-136, September 2023, doi: 10.1109/MCOM.001.2200679

    work page doi:10.1109/mcom.001.2200679 2023