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Deep reinforcement learning-driven optimization for UAV-enabled wireless networks
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School of Electrical Engineering |
Doctoral thesis (article-based)
| Defence date: 2025-10-10
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Language
en
Pages
102 + app.79
Series
Aalto University publication series Doctoral Theses, 194/2025
Abstract
Unmanned Aerial Vehicles (UAVs) have become essential components of modern wireless networks, supporting critical applications such as emergency communications, Internet of Things (IoT) deployments, surveillance, and disaster recovery. UAV-enabled wireless networks leverage UAVs' inherent advantages, including high mobility, rapid deployment, and superior line-of-sight communication, significantly enhancing network coverage, robustness, and operational efficiency. Nevertheless, these advantages introduce intricate optimization challenges, particularly in dynamic UAV deployment scenarios, complex trajectory planning, and interdependent resource management decisions. Traditional static optimization methods face difficulties in effectively managing these dynamic, non-convex optimization problems characterized by highly coupled variables, necessitating more advanced and adaptive solutions. Deep Reinforcement Learning (DRL) addresses these challenges effectively by integrating deep learning’s robust feature extraction capabilities with reinforcement learning’s adaptive decision-making strength.
This thesis examines DRL-driven optimization in three critical scenarios of UAV-enabled wireless communication applications. First, we explore dynamic multi-UAV deployment for adaptive wireless coverage, aiming to optimize UAV operational modes, transmission power, and movement strategies to balance power consumption and ground user coverage effectively. A multi-modal feature-based DRL model addresses these coupled optimization challenges efficiently. Second, we address UAV-assisted data collection in backscatter wireless sensor networks, where a UAV equipped with a directional movable antenna (MA) enhances communication efficiency. A tailored DRL approach is employed to jointly optimize UAV trajectory and MA orientation, minimizing the total data collection time and associated energy consumption. Lastly, we focus on UAV-enabled integrated sensing and communication (ISAC) systems designed for time-critical missions. The primary objective is to minimize the age of information (AoI) by jointly optimizing UAV trajectory planning and beamforming strategies. A DRL framework incorporating Kalman filtering for target tracking and regularized zero-forcing beamforming is developed, effectively balancing sensing accuracy with communication quality. Overall, this thesis establishes adaptive DRL frameworks specific to diverse UAV scenarios, enhancing the adaptability, efficiency, and performance of UAVenabled wireless networks.
Description
Supervising professor
Jäntti, Riku, Prof., Aalto University, Department of Information and Communications Engineering, FinlandThesis advisor
Chang, Zheng, Prof., University of Jyväskylä, FinlandOther note
Parts
- [Publication 1]: Yu Bai, Hui Zhao, Xin Zhang, Zheng Chang, Riku Jäntti, Kun Yang. Toward autonomous multi-UAV wireless network: A survey of reinforcement learning-based approaches. IEEE Communications Surveys & Tutorials, vol. 25, no. 4, pp. 3038 - 3067, October 2023.
Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202401041156DOI: 10.1109/COMST.2023.3323344 View at publisher
- [Publication 2]: Yu Bai, Zheng Chang, and Riku Jäntti. Deep Reinforcement Learningenabled Dynamic UAV Deployment and Power Control in Multi-UAV Wireless Networks. In IEEE International Conference on Communications (ICC), Denver, USA, pp. 1286-1290, June 2024.
Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202409116337DOI: 10.1109/ICC51166.2024.10622465 View at publisher
- [Publication 3]: Yu Bai, Boxuan Xie, Ying Liu, Zheng Chang, and Riku Jäntti. Dynamic UAV Deployment in Multi-UAV Wireless Networks: A Multi-Modal Feature-Based Deep Reinforcement Learning Approach. IEEE Internet of Things Journal, April 2025.
Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202506245280DOI: 10.1109/JIOT.2025.3556300 View at publisher
- [Publication 4]: Yu Bai, Boxuan Xie, Ruifan Zhu, Zheng Chang, and Riku Jäntti. Movable Antenna-Equipped UAV for Data Collection in Backscatter Sensor Networks: A Deep Reinforcement Learning-based Approach. Accepted for publication in IEEE International Conference on Communications (ICC), Montreal, Canada, pp. 1-6, June 2025.
DOI: 10.48550/arXiv.2411.13970 View at publisher
- [Publication 5]: Yu Bai, Yifan Zhang, Boxuan Xie, Zheng Chang, Yanru Zhang, Riku Jäntti, and Zhu Han. Age of Information Minimization in UAV-Enabled Integrated Sensing and Communication Systems. Submitted to IEEE Transactions on Mobile Computing, June 2025.
DOI: 10.48550/arXiv.2507.14299 View at publisher