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Learning with Dynamics: Autonomous Regulation of UAV Based Communication Networks with Dynamic UAV Crew

Ran Zhang, Bowei Li, Liyuan Zhang, Jiang, Xie, Miao Wang

TL;DR

This work addresses the challenge of regulating UAV-based communication networks under dynamically changing UAV crews by proposing RL-based strategies that operate in both reactive and proactive modes. It develops a comprehensive framework distinguishing centralized DRL and distributed MARL approaches, and introduces methods to handle mixed action spaces, exploration around crew changes, and robustness to varying fleet sizes. For solar-powered UCNs, the authors propose a two-subproblem decomposition and a centralized DRL solution to jointly optimize serving roles and charging profiles, further enriching this with game-theoretic MARL for hybrid cooperation-competition among UAVs. The practical impact lies in enabling autonomous, scalable regulation of UCNs in dynamic environments, with potential extensions to generative AI and wireless charging technologies to enhance resilience and efficiency.

Abstract

Unmanned Aerial Vehicle (UAV) based communication networks (UCNs) are a key component in future mobile networking. To handle the dynamic environments in UCNs, reinforcement learning (RL) has been a promising solution attributed to its strong capability of adaptive decision-making free of the environment models. However, most existing RL-based research focus on control strategy design assuming a fixed set of UAVs. Few works have investigated how UCNs should be adaptively regulated when the serving UAVs change dynamically. This article discusses RL-based strategy design for adaptive UCN regulation given a dynamic UAV set, addressing both reactive strategies in general UCNs and proactive strategies in solar-powered UCNs. An overview of the UCN and the RL framework is first provided. Potential research directions with key challenges and possible solutions are then elaborated. Some of our recent works are presented as case studies to inspire innovative ways to handle dynamic UAV crew with different RL algorithms.

Learning with Dynamics: Autonomous Regulation of UAV Based Communication Networks with Dynamic UAV Crew

TL;DR

This work addresses the challenge of regulating UAV-based communication networks under dynamically changing UAV crews by proposing RL-based strategies that operate in both reactive and proactive modes. It develops a comprehensive framework distinguishing centralized DRL and distributed MARL approaches, and introduces methods to handle mixed action spaces, exploration around crew changes, and robustness to varying fleet sizes. For solar-powered UCNs, the authors propose a two-subproblem decomposition and a centralized DRL solution to jointly optimize serving roles and charging profiles, further enriching this with game-theoretic MARL for hybrid cooperation-competition among UAVs. The practical impact lies in enabling autonomous, scalable regulation of UCNs in dynamic environments, with potential extensions to generative AI and wireless charging technologies to enhance resilience and efficiency.

Abstract

Unmanned Aerial Vehicle (UAV) based communication networks (UCNs) are a key component in future mobile networking. To handle the dynamic environments in UCNs, reinforcement learning (RL) has been a promising solution attributed to its strong capability of adaptive decision-making free of the environment models. However, most existing RL-based research focus on control strategy design assuming a fixed set of UAVs. Few works have investigated how UCNs should be adaptively regulated when the serving UAVs change dynamically. This article discusses RL-based strategy design for adaptive UCN regulation given a dynamic UAV set, addressing both reactive strategies in general UCNs and proactive strategies in solar-powered UCNs. An overview of the UCN and the RL framework is first provided. Potential research directions with key challenges and possible solutions are then elaborated. Some of our recent works are presented as case studies to inspire innovative ways to handle dynamic UAV crew with different RL algorithms.
Paper Structure (16 sections, 6 figures)

This paper contains 16 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Overview of UCNs and the RL Framework
  3. Responsive Strategy Design to Dynamic UAV Crew in General UCNs
  4. Design of Key Elements in RL
  5. Algorithm Design with Promoted Exploration
  6. Algorithm Design with Enhanced Robustness
  7. Proactive UAV Control Strategy in Solar-Powered Self-Sustainable UCNs
  8. Algorithm Design and Problem Decomposition
  9. Fusion of Game Theory in MARL Framework with Hybrid Cooperate-Compete Relationship
  10. Case Studies
  11. Responsive Regulation with Centralized DRL in General UCNs
  12. Distributed Regulation with MARL in General UCNs
  13. Proactive UAV Control in Solar-Powered UCNs
  14. Conclusions and Future Outlook
  15. Open Issues
  16. ...and 1 more sections

Figures (6)

  • Figure 1: Network model and the underlying RL framework for UCNs.
  • Figure 2: Asynchronous parallel computing (APC) diagram.
  • Figure 3: UAV trajectories with dynamic user distribution in cases of UAV quit and join-in, respectively.
  • Figure 4: Optimal coverage of active UAVs when UAVs randomly quit and join in sequentially.
  • Figure 5: Dynamics of solar radiation and user service demand in a day.
  • ...and 1 more figures