近场感知通信一体化系统的感知与通信性能帕累托优化

张广驰; 谢志立; 崔苗; 武庆庆

doi:10.11999/JEIT250231

近场感知通信一体化系统的感知与通信性能帕累托优化

doi: 10.11999/JEIT250231 cstr: 32379.14.JEIT250231

1.
广东工业大学信息工程学院广州 510006
2.
上海交通大学信息工程学院上海 200240

基金项目: 广东省基础与应用基础研究基金(2023A1515011980, 2023A1515140003)，广东省科技计划( No.2022A0505050023)

详细信息

作者简介:
张广驰：男，教授，研究方向为无线通信与人工智能

谢志立：男，研究生，研究方向为通感一体化波束形成设计

崔苗：女，副教授，研究方向为新一代无线通信技术

武庆庆：男，副教授，研究方向为通感一体化和无人机空地网络

通讯作者:
崔苗　cuimiao@gdut.edu.cn

中图分类号: TN929.5
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- 被引次数: 0
出版历程
- 收稿日期: 2025-04-02
- 修回日期: 2025-09-15
- 网络出版日期: 2025-09-20

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

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)

摘要

摘要: 感知通信一体化(ISAC)是第6代移动通信的重要研究方向之一，它使无线通信网络具备了感知能力。超大规模多输入多输出(XL-MIMO)的研究使得通信研究从远场转向近场，但ISAC在近场区域的研究还不充分。该文针对近场区域中存在散射体干扰的场景，研究了多用户XL-MIMO ISAC系统的波束成形设计，致力于探讨ISAC系统中通感性能的折衷问题。为此，该文首先导出了感知互信息的一般形式，并通过引入辅助变量和舒尔补(Schur complement)将其转化为半定规划问题进行优化。针对复杂的多用户信干噪比(SINR)表达式，利用Dinkelbach算法将其转化为凸函数形式，以降低优化难度。进一步，提出一种多目标优化框架，旨在同时最大化多用户信干噪比和感知互信息，并通过构建系统效用函数求解帕累托最优解。仿真结果表明，所提方法能够平衡用户通信和目标感知性能，实现两者之间的最优折衷。研究还揭示了散射体在距离上对感知目标的干扰规律，为ISAC系统的设计提供了重要参考。
- 超大规模多输入多输出 /
- 近场感知通信一体化 /
- 散射体干扰 /
- 多目标优化
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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图 1 XL-MIMO ISAC系统模型

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图 2 通信、感知性能随权重变化图

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图 3 感知互信息和多用户通信速率近远场对比关系图

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图 4 用户平均速率随SNR变化关系图

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图 5 目标感知互信息随SNR变化关系图

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图 6 感知目标与散射体不同距离下的对比关系图

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