Pareto Optimization of Sensing and Communication Performance of Near-field Integrated Sensing and Communication System

ZHANG Guangchi; XIE Zhili; CUI Miao; WU Qingqing

doi:10.11999/JEIT250231

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ZHANG Guangchi, XIE Zhili, CUI Miao, WU Qingqing. Pareto Optimization of Sensing and Communication Performance of Near-field Integrated Sensing and Communication System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250231

Citation:

ZHANG Guangchi, XIE Zhili, CUI Miao, WU Qingqing. Pareto Optimization of Sensing and Communication Performance of Near-field Integrated Sensing and Communication System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250231

Citation:

ZHANG Guangchi, XIE Zhili, CUI Miao, WU Qingqing. Pareto Optimization of Sensing and Communication Performance of Near-field Integrated Sensing and Communication System[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250231

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Pareto Optimization of Sensing and Communication Performance of Near-field Integrated Sensing and Communication System

doi: 10.11999/JEIT250231 cstr: 32379.14.JEIT250231

1.
Guangdong University of Technology, Guangzhou 510006, China
2.
Shanghai Jiaotong University, Shanghai 200240, China

Funds: Guangdong Basic and Applied Basic Research Foundation (2023A1515011980, 2023A1515140003), Guangdong Science and Technology Plan Project (2022A0505050023)

Received Date: 2025-04-02
Rev Recd Date: 2025-09-15

Available Online: 2025-09-20

Abstract

Abstract

Objective With the rapid development of Sixth-Generation (6G) communication technology, Integrated Sensing And Communication (ISAC) systems are regarded as key enablers of emerging applications such as the Internet of Things, smart cities, and autonomous driving. High-precision communication and sensing are required under limited spectrum resources. However, most existing studies concentrate on the far-field region, where incomplete derivation of the sensing mutual information metric, neglect of scatterer interference, and insufficient consideration of communication–sensing trade-offs limit the flexibility of beamforming design and reduce practical effectiveness. As application scenarios expand, the demand for efficient integration of communication and sensing becomes more pronounced, particularly in near-field environments where scatterer interference strongly affects system performance In this work, beamforming design for near-field ISAC systems under scatterer interference is investigated. A general expression for sensing mutual information is derived, a multi-objective optimization problem is formulated, and auxiliary variables, the Schur complement, and the Dinkelbach algorithm are employed to obtain Pareto optimal solutions. The proposed method provides a flexible and effective approach for balancing communication and sensing performance, thereby enhancing overall system performance and resource utilization in diverse application scenarios. The findings serve as a valuable reference for the optimal trade-off design of communication and sensing in near-field ISAC systems. Methods The proposed beamforming design method first derives a general expression for sensing mutual information in near-field scenarios, explicitly accounting for and quantifying the effect of scatterer interference on sensing targets. A multi-objective optimization problem is then formulated, with the Signal-to-Interference-plus-Noise Ratio (SINR) of communication users and sensing mutual information as objectives. Within this multi-objective framework, communication and sensing performance can be flexibly balanced to satisfy the requirements of different application scenarios. To enable tractable optimization, the sensing mutual information expression is transformed into a Semi-Definite Programming (SDP) problem using auxiliary variables and the Schur complement. Multi-user SINR expressions are reformulated with the Dinkelbach algorithm to convert them into convex functions, facilitating efficient optimization. The multi-objective problem is subsequently reduced to a single-objective one by constructing a system utility function, and the Pareto optimal solution is obtained to achieve the optimal balance between communication and sensing performance. This method provides a flexible and effective design strategy for near-field ISAC systems, substantially enhancing overall system performance and resource utilization. Results and Discussions This study presents a beamforming design method that balances communication and sensing performance through innovative optimization strategies. The method derives the general expression of sensing mutual information under scatterer interference, formulates a multi-objective optimization problem with the SINR of communication users and sensing mutual information as objectives, and transforms the problem into a convex form using auxiliary variables, the Schur complement, and the Dinkelbach algorithm. The Pareto optimal solution is then obtained via a system utility function, enabling the optimal balance between communication and sensing performance. Simulation results demonstrate that adjusting the weight parameter ρ flexibly balances user communication and target sensing performance (Fig. 2). As ρ increases from 0 to 1, sensing mutual information rises while user rate decreases, showing that a controllable trade-off can be achieved by tuning weights. In multi-user scenarios, near-field ISAC systems exhibit superior performance compared with far-field systems (Fig. 3). Under near-field conditions, the proposed method achieves more flexible and adjustable trade-offs than the classic Zero-Forcing (ZF) algorithm and single-objective optimization algorithms (Fig. 4, Fig. 5), confirming its effectiveness and superiority in practical applications. Furthermore, the study reveals the interference pattern of scatterers on sensing targets with respect to distance (Fig. 6, Fig. 7). The results indicate that the greater the distance difference between a scatterer and a sensing target, the weaker the interference on the target, with sensing mutual information gradually increasing and eventually converging. This finding provides a valuable reference for the design of near-field ISAC systems. Conclusions This paper proposes a beamforming design method for balancing communication and sensing performance by jointly optimizing sensing mutual information and communication rate. The method derives the general form of sensing mutual information, reformulates it as a SDP problem, and applies the Dinkelbach algorithm to process multi-user SINR expressions, thereby establishing a multi-objective optimization framework that can flexibly adapt to diverse application requirements. The results demonstrate three key findings: (1) The method enables flexible adjustment of communication and sensing performance, achieving an optimal trade-off through weight tuning, and allowing dynamic adaptation of system performance to specific application needs. (2) It reveals the interference pattern of scatterers on sensing targets with respect to distance, providing critical insights for near-field ISAC system design and supporting optimized system layout and parameter selection in complex environments. (3) In multi-user scenarios, the proposed approach outperforms traditional single-objective optimization methods in both communication rate and sensing mutual information, highlighting its competitiveness and practical value.

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References(22)

References

[1]	LU Haiquan, ZENG Yong, YOU Changsheng, et al. A tutorial on near-field XL-MIMO communications toward 6G[J]. IEEE Communications Surveys & Tutorials, 2024, 26(4): 2213–2257. doi: 10.1109/COMST.2024.3387749.
[2]	MA Dingyou, SHLEZINGER N, HUANG Tianyao, et al. Joint radar-communication strategies for autonomous vehicles: Combining two key automotive technologies[J]. IEEE Signal Processing Magazine, 2020, 37(4): 85–97. doi: 10.1109/MSP.2020.2983832.
[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. doi: 10.1109/JSTSP.2021.3113120.
[4]	YANG Wanning, LI Ming, and LIU Qian. A practical channel estimation strategy for XL-MIMO communication systems[J]. IEEE Communications Letters, 2023, 27(6): 1580–1583. doi: 10.1109/LCOMM.2023.3266821.
[5]	LU Haiquan and ZENG Yong. Communicating with extremely large-scale array/surface: Unified modeling and performance analysis[J]. IEEE Transactions on Wireless Communications, 2022, 21(6): 4039–4053. doi: 10.1109/TWC.2021.3126384.
[6]	DE CARVALHO E, ALI A, AMIRI A, et al. Non-stationarities in extra-large-scale massive MIMO[J]. IEEE Wireless Communications, 2020, 27(4): 74–80. doi: 10.1109/MWC.001.1900157.
[7]	DONG Zhenjun and ZENG Yong. Near-field spatial correlation for extremely large-scale array communications[J]. IEEE Communications Letters, 2022, 26(7): 1534–1538. doi: 10.1109/LCOMM.2022.3170735.
[8]	CHANG Hengtai, WANG Chengxiang, BIAN Ji, et al. A novel 3D beam domain channel model for UAV massive MIMO communications[J]. IEEE Transactions on Wireless Communications, 2023, 22(8): 5431–5445. doi: 10.1109/TWC.2023.3233961.
[9]	HAN Yu, JIN Shi, WEN Chaokai, et al. Channel estimation for extremely large-scale massive MIMO systems[J]. IEEE Wireless Communications Letters, 2020, 9(5): 633–637. doi: 10.1109/LWC.2019.2963877.
[10]	ZHANG Yunpu, WU Xun, and YOU Changsheng. Fast near-field beam training for extremely large-scale array[J]. IEEE Wireless Communications Letters, 2022, 11(12): 2625–2629. doi: 10.1109/LWC.2022.3212344.
[11]	LIU Fan, MASOUROS C, LI Ang, et al. MU-MIMO communications with MIMO radar: From co-existence to joint transmission[J]. IEEE Transactions on Wireless Communications, 2018, 17(4): 2755–2770. doi: 10.1109/TWC.2018.2803045.
[12]	LIU Fan, ZHOU Longfei, MASOUROS C, et al. Toward dual-functional radar-communication systems: Optimal waveform design[J]. IEEE Transactions on Signal Processing, 2018, 66(16): 4264–4279. doi: 10.1109/TSP.2018.2847648.
[13]	TSINOS C G, ARORA A, CHATZINOTAS S, et al. Joint transmit waveform and receive filter design for dual-function radar-communication systems[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(6): 1378–1392. doi: 10.1109/JSTSP.2021.3112295.
[14]	DOU Chenglong, HUANG Ning, WU Yuan, et al. Integrated sensing and communication enabled multidevice multitarget cooperative sensing: A fairness-aware design[J]. IEEE Internet of Things Journal, 2024, 11(17): 29190–29201. doi: 10.1109/JIOT.2024.3406930.
[15]	LI Jin, ZHOU Gui, GONG Tantao, et al. A framework for mutual information-based MIMO integrated sensing and communication beamforming design[J]. IEEE Transactions on Vehicular Technology, 2024, 73(6): 8352–8366. doi: 10.1109/TVT.2024.3355899.
[16]	MENG Chunwei, WEI Zhiqing, MA Dingyou, et al. Multiobjective-optimization-based transmit beamforming for multitarget and multiuser MIMO-ISAC systems[J]. IEEE Internet of Things Journal, 2024, 11(18): 29260–29274. doi: 10.1109/JIOT.2024.3413687.
[17]	WANG Zhaolin, MU Xidong, and LIU Yuanwei. Near-field integrated sensing and communications[J]. IEEE Communications Letters, 2023, 27(8): 2048–2052. doi: 10.1109/LCOMM.2023.3280132.
[18]	QU Kaiqian, GUO Shuaishuai, SAEED N, et al. Near-field integrated sensing and communication: Performance analysis and beamforming design[J]. IEEE Open Journal of the Communications Society, 2024, 5: 6353–6366. doi: 10.1109/OJCOMS.2024.3470844.
[19]	ZHAO Boqun, OUYANG Chongjun, LIU Yuanwei, et al. Modeling and analysis of near-field ISAC[J]. IEEE Journal of Selected Topics in Signal Processing, 2024, 18(4): 678–693. doi: 10.1109/JSTSP.2024.3386054.
[20]	YANG Yang and BLUM B S. MIMO radar waveform design based on mutual information and minimum mean-square error estimation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(1): 330–343. doi: 10.1109/TAES.2007.357137.
[21]	GAO Peng, LIAN Lixiang, and YU Jinpei. Cooperative ISAC with direct localization and rate-splitting multiple access communication: A Pareto optimization framework[J]. IEEE Journal on Selected Areas in Communications, 2023, 41(5): 1496–1515. doi: 10.1109/JSAC.2023.3240714.
[22]	LUO Zhiquan, MA W K, SO A M C, et al. Semidefinite relaxation of quadratic optimization problems[J]. IEEE Signal Processing Magazine, 2010, 27(3): 20–34. doi: 10.1109/MSP.2010.936019.