Intelligent joint communication and computation scheme of UAV-assisted offloading in high speed rail scenarios✩

Xiqi Cheng; Jingxuan Zhang; Xiaodong Xu; Shujun Han; Bizhu Wang; Mengyin Sun; Ping Zhang

doi:10.1016/j.dcan.2024.09.002

›› 2026, Vol. 12 ›› Issue (4) :594 -606. DOI: 10.1016/j.dcan.2024.09.002

Research Article

research-article

Intelligent joint communication and computation scheme of UAV-assisted offloading in high speed rail scenarios^✩

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^a Beijing University of Posts and Telecommunications , 100876, China

^b Beijing University of Science and Technology , 100083, China

^* E-mail address: xuxiaodong@bupt.edu.cn (X. Xu).

Xiqi Cheng received the B.S. degree in Communication Engineering from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2022. He is currently pursuing the Ph.D. degree in Information and Communication Engineering in Beijing University of Posts and Telecommunications (BUPT), Beijing, China. His research interests include HSR Communication, Mobile Edge Computation, Physical Layer Authentication, machine learning, and CSI generation.

Jingxuan Zhang (S’18-M’22) received the B.S. degree in Communication Engineering from Beijing Information Science and Technology University, Beijing, China, in 2017. She received her Ph.D. degrees of Information and Communication Engineering in Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2022. From June 2021 to February 2022, she visited the Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore. From 2022 to 2024, she was a Post-Doctoral Researcher at BUPT. In 2024, she joined the University of Science and Technology Beijing (USTB), Beijing, China, as a lecturer. Her research interests include ultra-Reliable Low Latency Communication, semantic communication, self-organization network, machine learning and resource management in 6G networks.

Xiaodong Xu (S’06-M’07-SM’18) received his B.S degree in Information and Communication Engineering and Master’s Degree in Communication and Information System both from Shandong University in 2001 and 2004 separately. He received his Ph.D. degrees of Circuit and System in BUPT in 2007. He is currently a professor of BUPT and a member of IMT-2030 (6G) Experts Panel. He has coauthored nine books/chapters and more than 150 journal and conference papers. He is also the inventor or co-inventor of 107 granted patents. His research interests cover semantic communications, intellicise communication system and satellite communications.

Shujun Han (S’18-M’20) received the Bachelor degree in Communication Engineering and the Master degree in Electronics and Communication Engineering from Zhengzhou University, Zhengzhou, China, in 2013 and 2016, respectively. She received the Ph.D. degree in Information and Telecommunications Engineering from Beijing University of Posts and Telecommunications (BUPT), Beijing, China, in 2020. From 2018 to 2019, she visited the 5G Innovation Centre (5GIC), University of Surrey, UK. From 2020 to 2023, she was a Post-Doctoral Researcher at BUPT. She is currently an Associate Researcher at BUPT. Her research interests are in the next-generation mobile communication systems, with a current emphasis on ComAI (AI and Communication, Semantic Communication, Edge Intelligence), and Massive Ultra-Reliable and Low Latency communications.

Bizhu Wang received a B.Eng. degree from the Beijing University of Posts and Telecommunications in 2016 and a PhD degree from Queen Mary University of London in 2021. Since 2021, she has been working as a lecturer at the Beijing University of Posts and Telecommunications. Her research interests cover semantic communication, network security, and Large Language Models (LLMs).

Mengying Sun (Member, IEEE) received the B.S. degree in communication engineering from Beijing University of Chemical Technology, Beijing, China, in 2016, and the Ph.D. degree in information and telecommunications engineering from Beijing University of Posts and Telecommunications (BUPT), Beijing, in 2022. From March 2021 to March 2022, she was a visiting Ph.D. student with the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada. She is currently an Assistant Professor with BUPT. Her research interests include mobile-edge computing, semantic communications, and AI-driven networking and communications.

Ping Zhang is currently a Professor with BUPT, the Director of the State Key Laboratory of Networking and Switching Technology, the Director of the Department of Broadband Communication of Peng Cheng Laboratory, a member of IMT-2020 (5G) Experts Panel, and a member of Experts Panel for China’s 6G development. He served as a Chief Scientist of National Basic Research Program (973 Program), an expert in Information Technology Division of National High-Tech R&D Program (863 Program), and a member of Consultant Committee on International Cooperation of National Natural Science Foundation of China. His research interests mainly focus on wireless communication. Prof. Zhang is an Academician of the Chinese Academy of Engineering.

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Abstract

With the continuous development of communication and computation technologies, large bandwidth services place higher demands on computing capacity and throughput in High-Speed Rail (HSR) scenarios. On the one hand, Millimetre-Wave enables high data transmission rates, but leads to high Doppler frequency deviation as well as large path loss. On the other hand, the development of Mobile Edge Computing greatly alleviates user computing congestion. In this paper, we propose the Adaptive Joint Communication and Computation Resource Allocation scheme to solve the energy optimization problem of a dual-band UAV and Mobile Relay relay-assisted HSR offloading system. This scheme works well to optimize the performance of the system through resource allocation. In addition, since the optimization problem is modeled as a Mixed-Integer Nonlinear Program, we propose the Pre- and Post-state connected Parameterized Deep Q-Network algorithm, which is based on Deep Reinforcement Learning approach, for offloading decision and bandwidth resource allocation. Simulation results show that the proposed algorithms result in lower system energy consumption, while ensuring a high task completion rate as well.

Keywords

Unmanned aerial vehicle relay / High-speed rail communication / Mobile edge computing / Dual-band communication / Resource allocation / Deep reinforce learning

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Xiqi Cheng, Jingxuan Zhang, Xiaodong Xu, Shujun Han, Bizhu Wang, Mengyin Sun, Ping Zhang. Intelligent joint communication and computation scheme of UAV-assisted offloading in high speed rail scenarios^✩. , 2026, 12 (4) : 594-606 DOI:10.1016/j.dcan.2024.09.002

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CRediT authorship contribution statement

Xiqi Cheng: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Jingxuan Zhang: Writing – review & editing. Xiaodong Xu: Writing – review & editing. Shujun Han: Writing – review & editing. Bizhu Wang: Writing – review & editing. Mengyin Sun: Writing – review & editing. Ping Zhang: Writing – review & editing.

Declaration of competing interest

The authors declare that they have no competing interests.

Acknowledgements

This work was supported in part by the National Key R&D Program of China No. 2020YFB1806905, in part by the National Natural Science Foundation of China No. 62401078, in part by China Postdoctoral Science Foundation Funded Project No. 2023M740342.

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	R. Chen, W.—X. Long, G. Mao, C. Li, Development trends of mobile communication systems for railways, IEEE Commun. Surv. Tutor. 20 (4) (2018) 3131-3141.

[2]	F. Spinelli, V. Mancuso, Toward enabled industrial verticals in 5g: a survey on mec—based approaches to provisioning and flexibility, IEEE Commun. Surv. Tutor. 23 (1) (2020) 596-630.

[3]	Z. Zhao, S. Bu, T. Zhao, Z. Yin, M. Peng, Z. Ding, T.Q. Quek, On the design of computation offloading in fog radio access networks, IEEE Trans. Veh. Technol. 68 (7) (2019) 7136-7149.

[4]	M. Elkashlan, T.Q. Duong, H.—H. Chen, Millimeter—wave communications for 5G: fundamentals: Part I [guest editorial], IEEE Commun. Mag. 52 (9) (2014) 52-54.

[5]	X. Ge, H. Cheng, M. Guizani, T. Han, 5G wireless backhaul networks: challenges and research advances, IEEE Netw. 28 (6) (2014) 6-11.

[6]	S. Singh, R. Mudumbai, U. Madhow, Interference analysis for highly directional 60—ghz mesh networks: the case for rethinking medium access control, IEEE/ACM Trans. Netw. 19 (5) (2011) 1513-1527.

[7]	X. Zhang, Y. Niu, S. Mao, Y. Cai, R. He, B. Ai, Z. Zhong, Y. Liu, Resource allocation for millimeter—wave train—ground communications in high—speed railway scenarios, IEEE Trans. Veh. Technol. 70 (5) (2021) 4823-4838.

[8]	C.—X. Wang, A. Ghazal, B. Ai, Y. Liu, P. Fan, Channel measurements and models for high—speed train communication systems: a survey, IEEE Commun. Surv. Tutor. 18 (2) (2015) 974-987.

[9]	C. Chen, Y. Niu, S. Mao, X. Zhang, Z. Han, B. Ai, M. Gao, H. Xiao, N. Wang, Coalition game based user association for mmwave mobile relay systems in rail traffic scenarios, IEEE Trans. Veh. Technol. 70 (10) (2021) 10528-10540.

[10]	Y. Ma, G. Ma, N. Wang, Z. Zhong, B. Ai, OTFS—TSMA for massive Internet of things in high—speed railway, IEEE Trans. Wirel. Commun. 21 (1) (2021) 519-531.

[11]	Y. Ma, Y. Niu, Z. Han, B. Ai, K. Li, Z. Zhong, N. Wang, Robust transmission scheduling for UAV—assisted millimeter—wave train—ground communication system, IEEE Trans. Veh. Technol. 71 (11) (2022) 11741-11755.

[12]	Q. Wu, Y. Zeng, R. Zhang, Joint trajectory and communication design for multi—UAV enabled wireless networks, IEEE Trans. Wirel. Commun. 17 (3) (2018) 2109-2121.

[13]	D. Yang, Q. Wu, Y. Zeng, R. Zhang, Energy tradeoff in ground—to—UAV communication via trajectory design, IEEE Trans. Veh. Technol. 67 (7) (2018) 6721-6726.

[14]	C. Zhan, Y. Zeng, R. Zhang, Energy—efficient data collection in UAV enabled wireless sensor network, IEEE Wirel. Commun. Lett. 7 (3) (2017) 328-331.

[15]	M. Mozaffari, W. Saad, M. Bennis, M. Debbah, Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage, IEEE Commun. Lett. 20 (8) (2016) 1647-1650.

[16]	S. Jeong, O. Simeone, J. Kang, Mobile edge computing via a UAV—mounted cloudlet: optimization of bit allocation and path planning, IEEE Trans. Veh. Technol. 67 (3) (2017) 2049-2063.

[17]	S. Taimoor, L. Ferdouse, W. Ejaz, Holistic resource management in UAV—assisted wireless networks: an optimization perspective, J. Netw. Comput. Appl. 205 (2022) 103439.

[18]	J. Chen, P. Chen, Q. Wu, Y. Xu, N. Qi, T. Fang, A game—theoretic perspective on resource management for large—scale UAV communication networks, China Commun. 18 (1) (2021) 70-87.

[19]	G. Ding, Q. Wu, L. Zhang, Y. Lin, T.A. Tsiftsis, Y.—D. Yao, An amateur drone surveillance system based on the cognitive Internet of things, IEEE Commun. Mag. 56 (1) (2018) 29-35.

[20]	M. Zhang, H. Cheng, P. Yang, C. Dong, H. Zhao, Q. Wu, T.Q. Quek, Adaptive routing design for flying ad hoc networks: a joint prediction approach, IEEE Trans. Veh. Technol. 73 (2) (2024) 2593-2604.

[21]	Q. Wu, J. Sheng, C. Wu, J. Zhang, Y. Wang, Research on UAV networking technology for high—speed railway emergency communication, in: 2021 International Wireless Communications and Mobile Computing (IWCMC), IEEE, 2021, pp. 1557-1562.

[22]	Y. Wang, Y. Niu, H. Wu, S. Mao, B. Ai, Z. Zhong, N. Wang, Scheduling of uav—assisted millimeter wave communications for high—speed railway, IEEE Trans. Veh. Technol. 71 (8) (2022) 8756-8767.

[23]	J. Wu, Z. Cao, Y. Zhang, X. Zhang, Edge—cloud collaborative computation offloading model based on improved partical swarm optimization in MEC, in: 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), IEEE, 2019, pp. 959-962.

[24]	L. Li, Z. Kuang, A. Liu, Energy efficient and low delay partial offloading scheduling and power allocation for MEC, in: ICC 2019—2019 IEEE International Conference on Communications (ICC), IEEE, 2019, pp. 1-6.

[25]	K. Cheng, Y. Teng, W. Sun, A. Liu, X. Wang, Energy—efficient joint offloading and wireless resource allocation strategy in multi—MEC server systems, in: 2018 IEEE International Conference on Communications (ICC), IEEE, 2018, pp. 1-6.

[26]	R. Yu, J. Ding, S. Maharjan, S. Gjessing, Y. Zhang, D.H. Tsang, Decentralized and optimal resource cooperation in geo—distributed mobile cloud computing, IEEE Trans. Emerg. Top. Comput. 6 (1) (2015) 72-84.

[27]	Y. Wu, K. Ni, C. Zhang, L.P. Qian, D.H. Tsang, NOMA—assisted multi—access mobile edge computing: a joint optimization of computation offloading and time allocation, IEEE Trans. Veh. Technol. 67 (12) (2018) 12244-12258.

[28]	X. Wei, S. Wang, A. Zhou, J. Xu, S. Su, S. Kumar, F. Yang, Mvr: an architecture for computation offloading in mobile edge computing, in: 2017 IEEE International Conference on Edge Computing (EDGE), IEEE, 2017, pp. 232-235.

[29]	J. Wu, Z. Cao, Y. Zhang, X. Zhang, Edge—cloud collaborative computation offloading model based on improved partical swarm optimization in MEC, in: 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), IEEE, 2019, pp. 959-962.

[30]	L. Li, Z. Kuang, A. Liu, Energy efficient and low delay partial offloading scheduling and power allocation for MEC, in: ICC 2019—2019 IEEE International Conference on Communications (ICC), IEEE, 2019, pp. 1-6.

[31]	H. Xu, K. Li, J. Cheng, B. Jiang, H. Yu, Latency—aware secure transmission in mec—assisted hsr communication systems, EURASIP J. Adv. Signal Process. 2021 (2021) 1-21.

[32]	V. Tapio, M. Sonkki, M. Juntti, Self—interference cancelation in the presence of non—linear power amplifier and receiver iq imbalance, EURASIP J. Wirel. Commun. Netw. 2020 (1) (2020) 1-21.

[33]	Y. Wang, M. Sheng, X. Wang, L. Wang, J. Li, Mobile—edge computing: partial computation offloading using dynamic voltage scaling, IEEE Trans. Commun. 64 (10) (2016) 4268-4282.

[34]	Y. Mao, C. You, J. Zhang, K. Huang, K.B. Letaief, A survey on mobile edge computing: the communication perspective, IEEE Commun. Surv. Tutor. 19 (4) (2017) 2322-2358.

[35]	T. Baykas, C.—S. Sum, Z. Lan, J. Wang, M.A. Rahman, H. Harada, S. Kato, IEEE 802.15.3c: the first IEEE wireless standard for data rates over 1 Gb/s, IEEE Commun. Mag. 49 (7) (2011) 114-121.