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

Chang, Z., Guo, W., Guo, X., & Ristaniemi, T. (2020). Machine Learning-Based Resource Allocation for Multi-UAV Communications System. In Proceedings of the IEEE International Conference on Communications Workshops. IEEE. IEEE/CIC international conference on communications workshops in China - workshops. https://doi.org/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:

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: https://doi.org/10.1109/ICCWorkshops49005.2020.9145458

Publication open access: Not open

Publication channel open access: 

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: resourcing; machine learning; unmanned aerial vehicles; wireless technology

Contributing organizations

Related projects

Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 0