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Related Experiment Video

Updated: Jun 9, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Multi-Agent DRL for Air-to-Ground Communication Planning in UAV-Enabled IoT Networks.

Khalid Ibrahim Qureshi1, Bingxian Lu1, Cheng Lu1

  • 1Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian 116024, China.

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|October 26, 2024
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Summary

This study enhances drone communication networks by decoupling uplink and downlink user associations. This novel approach optimizes drone trajectory and user connections for improved network efficiency, especially in emergencies.

Keywords:
IoUAVsMADRLSDN

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Area of Science:

  • Wireless Communication
  • Network Engineering
  • Robotics

Background:

  • Existing unmanned aerial vehicle (UAV)-assisted networks couple uplink and downlink associations, leading to suboptimal performance in dynamic environments.
  • Unpredictable user demands and network conditions challenge the efficiency of traditional UAV communication systems.

Purpose of the Study:

  • To enhance sum-rate effectiveness in full-duplex UAV-assisted communication networks.
  • To decouple uplink and downlink associations for ground-based users (GBUs) to improve network efficiency.
  • To integrate UAV trajectory design and user association for maximizing network sum-rate efficiency.

Main Methods:

  • Formulated a comprehensive optimization problem for UAV trajectory and user association.
  • Reformulated the non-convex problem as a Partially Observable Markov Decision Process (POMDP).
  • Employed multi-agent deep reinforcement learning (MADRL), specifically the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm.

Main Results:

  • Achieved enhanced sum-rate effectiveness through decoupled uplink and downlink associations.
  • Enabled UAVs to make real-time decisions using local observations via POMDP.
  • Demonstrated efficient learning of optimal user associations and trajectory controls using MADDPG.

Conclusions:

  • The proposed hybrid MADRL framework balances centralized training with distributed execution for optimal UAV operations.
  • The decoupled association method significantly improves network efficiency in dynamic environments.
  • The solution is highly applicable to critical scenarios like disaster response and search and rescue missions.