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Related Experiment Video

Updated: Jan 16, 2026

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Decentralized resource allocation in UAV communication networks through reward based multi agent learning.

Muhammad Shoaib1, Ghassan Husnain2, Muhsin Khan3

  • 1Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, 25100, Pakistan.

Scientific Reports
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

Unmanned aerial vehicles (UAVs) optimize wireless access by independently managing resources like users and power. A novel reward-based multi-agent learning (RMAL) framework maximizes system rewards without full information exchange.

Keywords:
Aerial base stationsDecentralized Decision-MakingDynamic resource allocationMulti-Agent learningUnmanned aerial vehicles

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Area of Science:

  • Wireless Communication Systems
  • Artificial Intelligence in Networking
  • Resource Management

Background:

  • Unmanned aerial vehicles (UAVs) offer flexible aerial base stations (ABS) for on-demand wireless access.
  • Dynamic resource allocation is crucial for maximizing performance in multi-UAV systems.
  • Existing methods may require extensive information exchange, increasing system overhead.

Purpose of the Study:

  • To investigate dynamic resource allocation strategies for multi-UAV-enabled communication systems.
  • To maximize the long-term rewards and overall system performance.
  • To develop a decentralized learning framework for UAV resource management.

Main Methods:

  • Modeling the resource allocation problem as a stochastic game with UAVs as learning agents.
  • Developing a reward-based multi-agent learning (RMAL) framework.
  • Implementing an agent-independent strategy using a Q-learning-based framework with local observations.

Main Results:

  • The proposed RMAL framework achieves effective resource allocation without requiring full information exchange between UAVs.
  • Simulation results indicate that RMAL performance is sensitive to parameter tuning but offers a good trade-off.
  • The method provides acceptable performance compared to systems with complete information sharing.

Conclusions:

  • The RMAL framework offers an efficient approach to dynamic resource allocation in multi-UAV systems.
  • Decentralized learning with local observations can achieve near-optimal performance while reducing communication overhead.
  • This research contributes to the advancement of intelligent resource management in aerial communication networks.