Trajectory Design and Resource Allocation for Multi-UAV Networks : Deep Reinforcement Learning Approaches
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
Published inIEEE Transactions on Network Science and Engineering
© 2022 the Authors
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. ...
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Publication in research information system
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Additional information about fundingThis work is partly supported by the National Natural Science Foundation of China (NSFC) under Grant 62071105. The work of Li You was supported in part by the Young Elite Scientist Sponsorship Program by China Institute of Communications, the Jiangsu Province Basic Research Project under Grant BK20192002, and the Fundamental Research Funds for the Central Universities.
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