A1 Journal article (refereed)
Trajectory Design and Resource Allocation for Multi-UAV Networks : Deep Reinforcement Learning Approaches (2022)

Chang, Z., Deng, H., You, L., Min, G., Garg, S., & Kaddoum, G. (2022). Trajectory Design and Resource Allocation for Multi-UAV Networks : Deep Reinforcement Learning Approaches. IEEE Transactions on Network Science and Engineering, Early Access. https://doi.org/10.1109/tnse.2022.3171600

JYU authors or editors

Publication details

All authors or editors: Chang, Zheng; Deng, Hengwei; You, Li; Min, Geyong; Garg, Sahil; Kaddoum, Georges

Journal or series: IEEE Transactions on Network Science and Engineering

eISSN: 2327-4697

Publication year: 2022

Volume: Early Access

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/tnse.2022.3171600

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/81007


The future mobile communication system is expected to provide ubiquitous connectivity and unprecedented services over billions of devices. The unmanned aerial vehicle (UAV), which is prominent in its flexibility and low cost, emerges as a significant network entity to realize such ambitious targets. In this work, novel machine learning-based trajectory design and resource allocation schemes are presented for a multi-UAV communications system. In the considered system, the UAVs act as aerial Base Stations (BSs) and provide ubiquitous coverage. In particular, with the objective to maximize the system utility over all served users, a joint user association, power allocation and trajectory design problem is presented. To solve the problem caused by high dimensionality in state space, we first propose a machine learning-based strategic resource allocation algorithm which comprises of reinforcement learning and deep learning to design the optimal policy of all the UAVs. Then, we also present a multi-agent deep reinforcement learning scheme for distributed implementation without knowing a priori knowledge of the dynamic nature of networks. Extensive simulation studies are conducted and illustrated to evaluate the advantages of the proposed scheme.

Keywords: wireless networks; unmanned aerial vehicles; machine learning; deep learning

Free keywords: resource management; trajectory; autonomous aerial vehicles; communication systems; reinforcement learning; wireless networks; throughput

Contributing organizations

Ministry reporting: No, publication in press

Preliminary JUFO rating: 1

Last updated on 2022-17-06 at 11:41