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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
Summary
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.
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.
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