A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
IEEE/CAA Journal of Automatica Sinica
| Citation: | L. Dong, H.-W. Kong, and X. Yuan, “Reinforcement learning-based spectral performance optimization for UAV-Assisted MIMO communication system,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125225
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S. K. Ibrahim, M. J. Singh, S. S. Al-Bawri, H. H. Ibrahim, M. T. Islam, M. S. Islam, A. Alzamil, and W. M. Abdulkawi, “Design, challenges and developments for 5G massive MIMO antenna systems at sub 6-GHz band: a review,” Nanomaterials, vol. 13, no. 3, p. 520, 2023. doi: 10.3390/nano13030520
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R. Hussain and M. S. Sharawi, “5G MIMO antenna designs for base station and user equipment: Some recent developments and trends,” IEEE Antennas and Propagation Magazine, vol. 64, no. 3, pp. 95–107, 2021.
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J. Kang, J. Chen, M. Xu, Z. Xiong, Y. Jiao, L. Han, D. Niyato, Y. Tong, and S. Xie, “Uav-assisted dynamic avatar task migration for vehicular metaverse services: A multi-agent deep reinforcement learning approach,” IEEE/CAA Journal of Automatica Sinica, vol. 11, no. 2, pp. 430–445, 2024. doi: 10.1109/JAS.2023.123993
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O. Dogru, J. Xie, O. Prakash, R. Chiplunkar, J. Soesanto, H. Chen, K. Velswamy, F. Ibrahim, and B. Huang, “Reinforcement Learning in Process Industries: Review and Perspective,” IEEE/CAA Journal of Automatica Sinica, vol. 11, no. 2, pp. 283–300, 2024. doi: 10.1109/JAS.2024.124227
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E. Kaufmann, L. Bauersfeld, A. Loquercio, M. Müller, V. Koltun, and D. Scaramuzza, “Champion-level drone racing using deep reinforcement learning,” Nature, vol. 620, no. 7976, pp. 982–987, 2023. doi: 10.1038/s41586-023-06419-4
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Y. Wang and Z. Gao, “Three-dimensional trajectory design for multi-user MISO UAV communications: A deep reinforcement learning approach,” in 2021 IEEE/CIC Int. Conf. on Communications in China (ICCC). IEEE, 2021, pp. 706–711.
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W. Zhang, C. Liu, G. Wang, and Y. Song, “The transmission scheme for UAV MIMO system based on reinforcement learning,” in 2022 14th Int. Conf. on Wireless Communications and Signal Processing (WCSP). IEEE, 2022, pp. 736–740.
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P. Consul, I. Budhiraja, D. Garg, and A. Bindle, “Power allocation scheme based on DRL for CF massive MIMO network with UAV,” in Innovations in Information and Communication Technologies: Proc. ICIICT 2022. Springer, 2022, pp. 33–43.
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H. Li, X. Lv, and S. Zhang, “Multi-objective deep reinforcement learning based joint beamforming and power allocation in UAV assisted cellular communication,” Wireless Personal Communications, vol. 134, no. 2, pp. 809–829, 2024. doi: 10.1007/s11277-024-10927-5
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R. Ding, F. Gao, and X. S. Shen, “3D UAV Trajectory Design and Frequency Band Allocation for Energy-Efficient and Fair Communication: A Deep Reinforcement Learning Approach,” IEEE Trans. Wireless Communications, vol. 19, no. 12, pp. 7796–7809, 2020. doi: 10.1109/TWC.2020.3016024
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C. Wang, D. Deng, L. Xu, and W. Wang, “Resource scheduling based on deep reinforcement learning in UAV assisted emergency communication networks,” IEEE Trans. Communications, vol. 70, no. 6, pp. 3834–3848, 2022. doi: 10.1109/TCOMM.2022.3170458
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S. Yu, Y. Lu, and X. Tao, “Deep Reinforcement Learning Based Transmission Scheme for UAV MIMO Communication,” in 2022 IEEE 22nd Int. Conf. on Communication Technology (ICCT). IEEE, 2022, pp. 481–485.
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