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Gateway Selection in Millimeter Wave UAV Wireless Networks Using Multi-Player Multi-Armed Bandit.

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  • 1Electrical Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addwasir 11991, Saudi Arabia.

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Summary

This study introduces battery-aware machine learning algorithms for unmanned aerial vehicle (UAV) networks. These algorithms efficiently select gateway UAVs to maximize data rates while minimizing energy consumption in disaster scenarios.

Keywords:
machine learningmillimeter wavemulti-armed banditunmanned aerial vehicles

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Area of Science:

  • Wireless Communications
  • Machine Learning Applications
  • Aerial Networks

Background:

  • Unmanned Aerial Vehicle (UAV)-based communication systems are crucial for post-disaster rescue services when terrestrial networks fail.
  • Access UAVs collect and relay information, requiring efficient selection of gateway UAVs for communication backhauling.
  • Millimeter wave (mmWave) links with antenna beamforming are utilized for high-capacity UAV backhaul.

Purpose of the Study:

  • To address the gateway UAV selection problem in decentralized UAV networks.
  • To maximize long-term average data rates for UAV relays.
  • To minimize the battery cost associated with UAV flights during gateway selection.

Main Methods:

  • The problem is modeled as a budget-constrained multi-player multi-armed bandit (MAB) problem.
  • Access UAVs act as players, gateway UAVs as arms, and rewards are data rates constrained by battery cost.
  • Three novel battery-aware MAB (BA-MAB) algorithms are proposed: Upper Confidence Bound (UCB), Thompson Sampling (TS), and Exponential Weight Algorithm for Exploration and Exploitation (EXP3).

Main Results:

  • The proposed BA-MAB algorithms demonstrate superior performance compared to random or nearest gateway selection methods.
  • Significant improvements are observed in total system data rates.
  • Enhanced energy efficiency is achieved through optimized gateway selection.

Conclusions:

  • Decentralized, selfish, and concurrent MAB strategies are effective for gateway UAV selection.
  • The developed BA-MAB algorithms provide an efficient solution for optimizing UAV communication networks.
  • This approach enhances both the data throughput and energy efficiency of UAV-assisted communication systems.