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Proactive Handover Decision for UAVs with Deep Reinforcement Learning.

Younghoon Jang1, Syed M Raza1, Moonseong Kim2

  • 1Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a Deep Reinforcement Learning (DRL) scheme to optimize Unmanned Aerial Vehicle (UAV) cellular network handovers. It significantly reduces unnecessary handovers, ensuring stable aerial connectivity.

Keywords:
Deep Reinforcement Learning (DRL)Proximal Policy Optimization (PPO)Unmanned Aerial Vehicles (UAV)handover decisionmobility management

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Aerospace Engineering

Background:

  • Unmanned Aerial Vehicles (UAVs) require continuous cellular connectivity for operations like surveillance and logistics.
  • Existing cellular handover policies are inadequate for UAVs due to fluctuating aerial signal strength.

Purpose of the Study:

  • To develop a novel handover decision scheme for UAVs using Deep Reinforcement Learning (DRL).
  • To minimize unnecessary handovers while ensuring stable cellular connectivity for UAVs.

Main Methods:

  • A DRL framework utilizing a proximal policy optimization algorithm was employed.
  • The system used UAV state as input and Received Signal Strength Indicator (RSSI) with a reward function for online learning.
  • Evaluated in a 3D-emulated UAV mobility environment.

Main Results:

  • Reduced unnecessary UAV handovers by up to 76% compared to greedy schemes and 73% compared to Q-learning.
  • Maintained RSSI above -75 dBm over 80% of the time, ensuring reliable communication.

Conclusions:

  • The proposed DRL handover scheme effectively addresses the challenges of UAV connectivity in cellular networks.
  • This approach enhances the reliability and efficiency of UAV operations through intelligent handover decisions.