Joint user association and cooperative beamforming for multi-IRSs aided mmWave communication systems✩

Qing Xue; Jiajun Mu; Fengsheng Wei; Meng Hua; Qianbin Chen

doi:10.1016/j.dcan.2025.11.007

›› 2026, Vol. 12 ›› Issue (2) :252 -261. DOI: 10.1016/j.dcan.2025.11.007

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Joint user association and cooperative beamforming for multi-IRSs aided mmWave communication systems^✩

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^a School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China

^b Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, 313001, Huzhou, PR China

^c Department of Electrical and Electronic Engineering, Imperial College London, SW7 2AZ, London, U.K

^* E-mail addresses: xueq@cqupt.edu.cn (Q. Xue), s220132118@stu.cqupt.edu.cn (J. Mu), weifengsheng@csj.uestc.edu.cn (F. Wei), m.hua@imperial.ac.uk (M. Hua), cqb@cqupt.edu.cn (Q. Chen).

Qing Xue received the PhD degree in information and communication engineering from Southwest Jiaotong University in 2018. She then joined the School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, China, where she is currently an Associate Professor. From Dec. 2019 to Jan. 2024, she was a post-doctoral fellow with the National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China. During this period, she joined the State Key Laboratory of Internet of Things for Smart City, University of Macau, for two years as a postdoctoral researcher with the support of the Macao Young Scholars Program. Her research interests include resource management in mobile networks, intelligent wireless networking, and Integrated Sensing and Communication.

Jiajun Mu received the bachelor’s degree from the Changchun University of Architecture and Civil Engineering in 2022, and the master’s degree in communication engineering from the Chongqing University of Posts and Telecommunications in 2025. His research interests include millimeter-wave communication, intelligent wireless networking, and resource management in mobile networks.

Fengsheng Wei received the BE, ME, and PhD degrees in communication and information engineering from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2012, 2016, and 2022, respectively. He completed his postdoctoral research at UESTC in 2025. He is currently a full-time researcher with the Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, China. His research interests include next-generation mobile communication sys-tems, machine learning, global optimization, and network slicing for wireless networks.

Meng Hua received the PhD degree in Information Science and Engineering from South-east University, Nanjing, China, in 2021. He was the recipient of the Outstanding PhD Thesis Award from the Chinese Institute of Electronics in 2021. From January 2021 to June 2023, he was a Postdoctoral Research Fellow with the University of Macau. From June 2023 to January 2024, he served as a Postdoctoral Fellow at the City University of Hong Kong. Since January 2024, he has been a Research Associate with Imperial College London. Dr. Hua has been listed among the Stanford/Elsevier World’s Top 2 % Scientists from 2022 to 2025. He currently serves as an Associate Editor for the IEEE Transactions on Communications and the IEEE Wireless Communications Letters. His research inter-ests include 6G goal-oriented AI, localization, integrated sensing and communication, and intelligent reflecting surface-assisted communications.

Qianbin Chen received the BS degree from Sichuan University, Chengdu, China, in 1988, and the PhD degree in electrical engineering from the University of Electronic Science and Technology of China, Chengdu, in 2006. He joined the School of Communications and Information Engineering, Chongqing University of Posts and Technology, Chongqing, China, where he is currently a Professor. He has been working in the areas of wireless and mobile networking for more than 20 years. He has authored more than 120 international journals and conference articles. His research interests include wireless communication, network theory, and multi-media technology.

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Abstract

This paper investigates a downlink millimeter-Wave (mmWave) communication system equipped with multi-ple cooperative Intelligent Reflecting Surfaces (IRSs), aiming to extend mmWave signal coverage and maximize system throughput. To fully exploit the potential of IRSs within a user-centric framework, this study delves into the joint optimization problem of user multiple association, transmit beamforming, and cooperative passive beamforming. Meanwhile, the impact of IRS locations on user association is analyzed. Given the non-convexity and complexity of the joint optimization problem, a low-complexity optimization algorithm is designed. The algorithm integrates iterative optimization, Lagrangian dual decomposition, and Fractional Programming (FP) techniques. Specifically, the user association problem is optimized using the Lagrangian dual decomposition method, while the joint beamforming is solved via the FP method. Simulation results demonstrate that, com-pared to traditional methods, the proposed algorithm significantly improves the system sum rate, validating its effectiveness and superiority.

Keywords

Intelligent reflecting surface / Millimeter wave communication / User association / Beamforming

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Qing Xue, Jiajun Mu, Fengsheng Wei, Meng Hua, Qianbin Chen. Joint user association and cooperative beamforming for multi-IRSs aided mmWave communication systems^✩. , 2026, 12(2): 252-261 DOI:10.1016/j.dcan.2025.11.007

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CRediT authorship contribution statement

Qing Xue: Writing-review & editing, Methodology, Conceptual-ization; Jiajun Mu: Writing-original draft, Software, Investigation; Fengsheng Wei: Validation, Supervision; Meng Hua: Writing-review & editing, Formal analysis, Supervision; Qianbin Chen: Writing-re-view & editing, Funding acquisition, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

This work was supported in part by the National Natural Science Foundation of China (U23A20279, 62561008), in part by the Natu-ral Science Foundation of Chongqing under Grant CSTB2024NSCQ-MSX0535, and in part by the Science and Technology Development Fund (001/2024/SKL) and the State Key Laboratory of Internet of Things for Smart City (University of Macau) Open Research Project (Ref. No.: SKL-IoTSC(UM)/ORP03/2026). Parts of this paper were presented at the 2025 IEEE 101st Vehicular Technology Conference (VTC2025-Spring) [DOI: 10.1109/VTC2025-Spring65109.2025.11174356].

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