[1]ZHAO Lin,CUI Wen,CHENG Jianhua.Joint resource allocation method for IRS-assisted integrated sensing and communication system[J].CAAI transactions on intelligent systems,2026,21(5):1300-1312.[doi:10.11992/tis.202601028]
Copy
Joint resource allocation method for IRS-assisted integrated sensing and communication system
CAAI transactions on intelligent systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
21
Number of periods:
2026 5
Page number:
1300-1312
Column:
学术论文—智能系统
Public date:
2026-09-05
- Title:
- Joint resource allocation method for IRS-assisted integrated sensing and communication system
- Keywords:
- integrated sensing and communication system; intelligent reflecting surface; sum-rate; energy efficiency; membrane-inspired quantum dung beetle optimizer algorithm; quantum computing; membrane computing; beamforming
- CLC:
- TP273
- DOI:
- 10.11992/tis.202601028
- Abstract:
- In integrated sensing and communication (ISAC) system, there are conflicting requirements that are low energy consumption and high spectral efficiency (SE). For ISAC system, aiming at improving energy efficiency (EE), we have introduced intelligent reflecting surface (IRS) in this paper. Balancing the transmission rate and energy consumption for IRS-assisted ISAC system, when reflect beamforming at IRS and transmit beamforming at ISAC base station are jointly designed, we have proposed a joint resource allocation method for IRS-assisted ISAC system. In this paper, a novel membrane-inspired quantum dung beetle optimizer algorithm (MQDBO) has been proposed to obtain optimal joint resource allocation method. To evaluate our newly proposed scheme, superiority and effectiveness in balancing transmission rate and energy consumption are effectively verified through simulation results. For IRS-assisted ISAC system, this novel scheme significantly improves sum-rate and energy efficiency. For different simulation scenarios, the proposed joint resource allocation method based on MQDBO can achieve the highest sum-rate and energy efficiency and has application prospects in the domain of wireless communication.
- References:
-
[1] 张平, 陈岩, 吴超楠. 6G: 新一代移动通信技术发展态势及展望[J]. 中国工程科学, 2023, 25(6): 1-8 ZHANG Ping, CHEN Yan, WU Chaonan. Six-generation mobile communication: development trend and outlook[J]. Strategic study of CAE, 2023, 25(6): 1-8
[2] ZHANG J A, RAHMAN M L, WU Kai, et al. Enabling joint communication and radar sensing in mobile networks: a survey[J]. IEEE communications surveys & tutorials, 2022, 24(1): 306-345
[3] ZHANG J A, LIU Fan, MASOUROS C, et al. An overview of signal processing techniques for joint communication and radar sensing[J]. IEEE journal of selected topics in signal processing, 2021, 15(6): 1295-1315
[4] 曾勇, 杨定邦, 李鑫睿, 等. 6G通感一体化: 从传统MIMO到稀疏MIMO[J]. 中山大学学报(自然科学版中英文), 2025, 64(4): 1-11 ZENG Yong, YANG Dingbang, LI Xinrui, et al. 6G integrated sensing and communications: from traditional MIMO to sparse MIMO[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2025, 64(4): 1-11
[5] LIU Fan, CUI Yuanhao, MASOUROS C, et al. Integrated sensing and communications: toward dual-functional wireless networks for 6G and beyond[J]. IEEE journal on selected areas in communications, 2022, 40(6): 1728-1767
[6] 朱政宇, 王梓晅, 徐金雷, 等. 智能反射面辅助的未来无线通信: 现状与展望[J]. 航空学报, 2022, 43(2): 203-217 ZHU Zhengyu, WANG Zixuan, XU Jinlei, et al. Future wireless communication assisted by intelligent reflecting surface: State of art and prospects[J]. Acta aeronautica et astronautica sinica, 2022, 43(2): 203-217
[7] SHAO Xiaodan, YOU Changsheng, ZHANG Rui. Intelligent reflecting surface aided wireless sensing: applications and design issues[J]. IEEE wireless communications, 2024, 31(3): 383-389
[8] QI Kangwei, WU Qiong, FAN Pingyi, et al. Reconfigurable-intelligent-surface-aided vehicular edge computing: joint phase-shift optimization and multiuser power allocation[J]. IEEE Internet of Things journal, 2025, 12(1): 764-777
[9] XU Fan, YAO Jiawei, LAI Wenhai, et al. Blind beamforming for coverage enhancement with intelligent reflecting surface[J]. IEEE transactions on wireless communications, 2024, 23(11): 15736-15752
[10] WANG Dawei, LI Jiawei, LV Qinyi, et al. Integrating reconfigurable intelligent surface and AAV for enhanced secure transmissions in IoT-enabled RSMA networks[J]. IEEE Internet of Things journal, 2025, 12(8): 9405-9419
[11] REN Zixiang, XU Jie, QIU Ling, et al. Secure cell-free integrated sensing and communication in the presence of information and sensing eavesdroppers[J]. IEEE journal on selected areas in communications, 2024, 42(11): 3217-3231
[12] MU Junsheng, ZHANG Ronghui, CUI Yuanhao, et al. UAV meets integrated sensing and communication: challenges and future directions[J]. IEEE communications magazine, 2023, 61(5): 62-67
[13] HUA Meng, WU Qingqing, CHEN Wen, et al. Secure intelligent reflecting surface-aided integrated sensing and communication[J]. IEEE transactions on wireless communications, 2024, 23(1): 575-591
[14] AMIRIARA H, MIRMOHSENI M, ELZANATY A, et al. A physical layer security framework for IRS-assisted integrated sensing and semantic communication systems[J]. IEEE transactions on cognitive communications and networking, 2025, 11(5): 2955-2969
[15] LI Heyi, WANG Hong, SHI Zheng. Sensing performance enhancement for reconfigurable intelligent surface empowered integrated sensing and communications[J]. IEEE transactions on vehicular technology, 2026, 75(1): 1085-1095
[16] AKDENIZ M R, LIU Yuanpeng, SAMIMI M K, et al. Millimeter wave channel modeling and cellular capacity evaluation[J]. IEEE journal on selected areas in communications, 2014, 32(6): 1164-1179
[17] 何子述, 程子扬, 李军, 等. 集中式MIMO雷达研究综述[J]. 雷达学报, 2022, 11(5): 805-829 HE Zishu, CHENG Ziyang, LI Jun, et al. A survey of collocated MIMO radar[J]. Journal of radars, 2022, 11(5): 805-829
[18] LIU Huimin, LI Yong, CHENG Wei, et al. Sensing-efficient transmit beamforming for ISAC with MIMO radar and MU-MIMO communication[J]. Remote sensing, 2024, 16(16): 3028
[19] GOH C Y, LEOW C Y, NORDIN R. Energy efficiency of unmanned aerial vehicle with reconfigurable intelligent surfaces: a comparative study[J]. Drones, 2023, 7(2): 98
[20] 高洪元, 刁鸣. 量子群智能及其在通信技术中的应用[M]. 北京: 电子工业出版社, 2016: 1-3.
[21] 高洪元, 李晨琬. 膜量子蜂群优化的多目标频谱分配[J]. 物理学报, 2014, 63(12): 460-469 GAO Hongyuan, LI Chenwan. Membrane-inspired quantum bee colony algorithm for multiobjective spectrum allocation[J]. Acta physica sinica, 2014, 63(12): 460-469
[22] MOHAMMADBEIGI A, MAROOSI A, HEMMATI M. Optimal chiller loading for energy conservation using a hybrid whale optimization algorithm based on population membrane systems[J]. International journal of modelling and simulation, 2022, 42(1): 101-116
[23] ZHANG Gexiang, GHEORGHE M, WU Chaozhong. A quantum-inspired evolutionary algorithm based on P systems for knapsack problem[J]. Fundamenta informaticae, 2008, 87(1): 93-116
[24] P?UN G. Computing with membranes[J]. Journal of computer and system sciences, 2000, 61(1): 108-143
[25] XUE Jiankai, SHEN Bo. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization[J]. The journal of supercomputing, 2023, 79(7): 7305-7336
[26] GONG Chen, ZHOU Nanrun, XIA Shuhua, et al. Quantum particle swarm optimization algorithm based on diversity migration strategy[J]. Future generation computer systems, 2024, 157: 445-458
[27] BICHARA R M, ASADALLAH F A B, AWAD M, et al. Quantum genetic algorithm for the design of miniaturized and reconfigurable IoT antennas[J]. IEEE transactions on antennas and propagation, 2023, 71(5): 3894-3904
[28] DEY A, BHATTACHARYYA S, DEY S, et al. A quantum inspired differential evolution algorithm for automatic clustering of real life datasets[J]. Multimedia tools and applications, 2024, 83(3): 8469-8498
[29] DHIMAN G, KUMAR V. Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems[J]. Knowledge-based systems, 2019, 165: 169-196.
[30] ZHANG Shuowen, ZHANG Rui. Capacity characterization for intelligent reflecting surface aided MIMO communication[J]. IEEE journal on selected areas in communications, 2020, 38(8): 1823-1838
[31] MA Teng, XIAO Yue, LEI Xia, et al. Integrated sensing and communication with reconfigurable intelligent surfaces[J]. IEEE transactions on vehicular technology, 2024, 73(12): 19051-19064
[32] 马晓双, 沈焕锋, 杨杰, 等. 极化SAR相干斑抑制的非局部加权最小均方误差滤波算法[J]. 中国图象图形学报, 2015, 20(1): 140-150 MA Xiaoshuang, SHEN Huanfeng, YANG Jie, et al. Polarimetric SAR speckle filtering using a nonlocal weighted minimum mean squared error filter[J]. Journal of image and graphics, 2015, 20(1): 140-150
[33] ELTVED A, DAHL J, ANDERSEN M S. On the robustness and scalability of semidefinite relaxation for optimal power flow problems[J]. Optimization and engineering, 2020, 21(2): 375-392
[34] HUANG Chongwen, ZAPPONE A, ALEXANDROPOULOS G C, et al. Reconfigurable intelligent surfaces for energy efficiency in wireless communication[J]. IEEE transactions on wireless communications, 2019, 18(8): 4157-4170
- Similar References:
Memo
-
Last Update:
2026-09-05