Deep Reinforcement Learning-enabled Dynamic UAV Deployment and Power Control in Multi-UAV Wireless Networks
Loading...
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Authors
Date
2024
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
Series
ICC 2024 - IEEE International Conference on Communications, pp. 1286-1291, IEEE International Conference on Communications
Abstract
Using Unmanned Aerial Vehicles (UAVs) as aerial base stations for providing services to ground users has received growing research interest in recent years. The dynamic deployment of UAVs represents a significant research direction within UAV network studies. This paper introduces a highly adaptable UAV wireless network that accounts for the mobility of UAVs and users, the variability in their states, and the tunable transmission power of UAVs. The objective is to maximize energy efficiency while ensuring the minimum number of unserved online users. This dual objective is achieved by jointly optimizing the states, transmission powers, and movement strategies of UAVs. To address the variable state challenges posed by the dynamic environment, user and UAV data is encapsulated within a multi-channel map. A Convolutional Neural Network (CNN) then processes this map to extract key features. The deployment and power control strategy are determined by an agent trained by the Proximal Policy Optimization (PPO)-based Deep Reinforcement Learning (DRL) algorithm. Simulation results demonstrate the effectiveness of the proposed strategy in enhancing energy efficiency and reducing the number of unserved online users.Description
Publisher Copyright: © 2024 IEEE.
Keywords
Other note
Citation
Bai, Y, Chang, Z & Jantti, R 2024, Deep Reinforcement Learning-enabled Dynamic UAV Deployment and Power Control in Multi-UAV Wireless Networks . in M Valenti, D Reed & M Torres (eds), ICC 2024 - IEEE International Conference on Communications . IEEE International Conference on Communications, IEEE, pp. 1286-1291, IEEE International Conference on Communications, Denver, Colorado, United States, 09/06/2024 . https://doi.org/10.1109/ICC51166.2024.10622465