QoS-Aware UAV-BS Deployment Optimization Based on Reinforcement Learning

We propose an unmanned aerial vehicle mounted base station (UAV-BS) deployment optimization scheme using reinforcement learning (RL). We formulate the objective function considering quality-of-service (QoS) of ground user equipments and fairness among them. To solve the optimization problem using RL...

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Bibliographic Details
Main Authors: Lee, Hyungje, Eom, Chahyeon, Lee, Chungyong
Format: Conference Proceeding
Language:English
Subjects:
Autonomous aerial vehicles
Base stations
deep Q-network (DQN)
Linear programming
Optimization
Quality of service
quality-of-service (QoS)
Reinforcement learning
Reinforcement learning (RL)
Simulation
UAV-BS deployment
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Description
Summary:We propose an unmanned aerial vehicle mounted base station (UAV-BS) deployment optimization scheme using reinforcement learning (RL). We formulate the objective function considering quality-of-service (QoS) of ground user equipments and fairness among them. To solve the optimization problem using RL, we propose a deep Q-network (DQN) model by defining the state, action, reward, and train UAV-BS to find the best movement direction. The simulation results show that the proposed DQN model is trained well, and the UAV-BS reaches the optimum position through our proposed DQN model.
ISSN:2767-7699
DOI:10.1109/ICEIC57457.2023.10049907