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Adaptive Q-Learning Trajectory Optimization for the Hybrid NOMA and OMA Assisted UAV Communications Network

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Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology (IoTCIT 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1197))

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Abstract

Benefit to the advantages of easy deployment and high flexibility, unmanned aerial vehicle (UAV) has been utilized to act as the aerial base station, providing communications service for target areas, such as remote regions and disaster areas. However, with the ever-increasing demand of high-speed and high-quality connections, the efficient multiple access constitutes the main challenge of the UAV communications network. Therefore, in this paper, we propose a hybrid multiple access strategy for UAV communications network, where both the non-orthogonal multiple access (NOMA) and the orthogonal multiple access (OMA) technology are invoked for the sake of efficiently handling the multi-users data-hungry connections. To expound, an adaptive Q-learning based trajectory optimization algorithm is conceived, which is capable of successively solving the problem of user clustering, power allocation and UAV’s trajectory, yielding the maximized achievable throughput. The numerical simulation results demonstrate that the proposed scheme has superiority in terms of average coverage and achievable rate, compared to that of the conventional OMA and NOMA schemes.

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Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant 62001219, and in part by the Key Research and Development project of Jiangsu Province under Grant BE2021013-4, and in part by the Shuangchuang Talent Program of Jiangsu Province under Grant JSSCBS20210159.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Simeng Feng, Yunyi Zhang & Kai Liu

  2. School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Baolong Li

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  1. Simeng Feng
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  2. Yunyi Zhang
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  3. Kai Liu
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  4. Baolong Li
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Corresponding author

Correspondence to Baolong Li .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, Hunan, China

    Jian Dong

  2. School of Information Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Long Zhang

  3. School of Information and Control Engineering, China University of Mining and Technolog, Xuzhou, Jiangsu, China

    Deqiang Cheng

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Feng, S., Zhang, Y., Liu, K., Li, B. (2024). Adaptive Q-Learning Trajectory Optimization for the Hybrid NOMA and OMA Assisted UAV Communications Network. In: Dong, J., Zhang, L., Cheng, D. (eds) Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology. IoTCIT 2023. Lecture Notes in Electrical Engineering, vol 1197. Springer, Singapore. https://doi.org/10.1007/978-981-97-2757-5_30

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  • DOI: https://doi.org/10.1007/978-981-97-2757-5_30

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2756-8

  • Online ISBN: 978-981-97-2757-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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