A4 Article in conference proceedings
Machine Learning-Based Resource Allocation for Multi-UAV Communications System (2020)


Chang, Zheng; Guo, Wenlong; Guo, Xijuan; Ristaniemi, Tapani (2020). Machine Learning-Based Resource Allocation for Multi-UAV Communications System. In Proceedings of the IEEE International Conference on Communications Workshops, IEEE/CIC international conference on communications workshops in China - workshops. IEEE. DOI: 10.1109/ICCWorkshops49005.2020.9145458


JYU authors or editors


Publication details

All authors or editors: Chang, Zheng; Guo, Wenlong; Guo, Xijuan; Ristaniemi, Tapani

Parent publication: Proceedings of the IEEE International Conference on Communications Workshops

Conference:

IEEE International Conference on Communications Workshops

Place and date of conference: Dublin, Ireland, 7.-11.6.2020

eISBN: 978-1-7281-7440-2

Journal or series: IEEE/CIC international conference on communications workshops in China - workshops

ISSN: 2474-9133

eISSN: 2474-9141

Publication year: 2020

Publisher: IEEE

Publication country: United States

Publication language: English

DOI: http://doi.org/10.1109/ICCWorkshops49005.2020.9145458

Open Access: Publication channel is not openly available


Abstract

The unmanned aerial vehicle (UAV)-based wireless communication system is prominent in its flexibility and low cost for providing ubiquitous connectivity. In this work, considering a multi-UAV communications system, we propose to utilize a machine learning-based approach to tackle the trajectory design and resource allocation problems. In particular, with the objective to maximize the system utility over all served ground users, a joint user association, power allocation and trajectory design problem is formulated. To solve the problem caused by high dimensionality in state space, the machine learning-based strategic resource allocation algorithm comprising of reinforcement learning and deep learning is presented to design the optimal policy of all the UAVs. Extensive simulation studies are conducted and illustrated to evaluate the advantages of the proposed scheme.


Keywords: resource allocation; machine learning; unmanned aerial vehicles; wireless technology

Free keywords: trajectory design; drone resource management; unmanned aerial vehicles


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Ministry reporting: Yes


Last updated on 2020-04-08 at 09:54