一种用于单站背景信道的新型多参考点建模框架：面向3GPP ISAC标准化
A Novel Multi-Reference-Point Modeling Framework for Monostatic Background Channel: Toward 3GPP ISAC Standardization
AI总结：
本文针对3GPP ISAC单站背景信道建模难题，基于28 GHz室内测量提出多参考点叠加随机模型及遗传算法优化框架，有效填补标准化空白并支持6G。
摘要：
集成感知与通信（ISAC）已被国际电信联盟（ITU）和3GPP确定为6G的关键应用。一个现实且与标准兼容的信道模型对于ISAC系统设计至关重要。为了表征感知目标（ST）的影响，3GPP将ISAC信道定义为目标信道与背景信道的组合，分别包含与ST相关的多径分量和仅源于环境的多径分量。尽管背景信道不携带直接的ST信息，但其精确建模对于评估感知性能至关重要，尤其是在复杂环境中。现有通信标准表征的是分离的发射机（Tx）与接收机（Rx）之间的传播。然而，在Tx和Rx共址的ISAC单站模式下对背景信道进行建模仍然是一个紧迫的挑战。本文首先在28 GHz下对室内场景进行了ISAC单站背景信道测量。提取了现实信道参数，揭示了显著的单跳传播和离散多径分布。受这些特性的启发，提出了一种新的随机模型，将ISAC单站背景信道表征为单站Tx&Rx与多个类似通信Rx的参考点（RP）之间子信道的叠加。该模型与标准化兼容，并引入了一个3GPP扩展实现框架。最后，提出了一种基于遗传算法的方法来提取多RP的最优数量和位置。通过比较实测与仿真的信道参数，验证了该优化方法和建模框架。结果表明，所提模型有效捕捉了单站背景信道特性，填补了ISAC信道建模中的一个关键空白，并支持6G标准化。
英文摘要：
Integrated Sensing and Communication (ISAC) has been identified as a key 6G application by ITU and 3GPP. A realistic, standard-compatible channel model is essential for ISAC system design. To characterize the impact of Sensing Targets (STs), 3GPP defines ISAC channel as a combination of target and background channels, comprising multipath components related to STs and those originating solely from the environment, respectively. Although the background channel does not carry direct ST information, its accurate modeling is critical for evaluating sensing performance, especially in complex environments. Existing communication standards characterize propagation between separated transmitter (Tx) and receiver (Rx). However, modeling background channels in the ISAC monostatic mode, where the Tx and Rx are co-located, remains a pressing challenge. In this paper, we firstly conduct ISAC monostatic background channel measurements for an indoor scenario at 28 GHz. Realistic channel parameters are extracted, revealing pronounced single-hop propagation and discrete multipath distribution. Inspired by these properties, a novel stochastic model is proposed to characterizing the ISAC monostatic background channel as the superposition of sub-channels between the monostatic Tx&Rx and multiple communication Rx-like Reference Points (RPs). This model is compatible with standardizations, and a 3GPP-extended implementation framework is introduced. Finally, a genetic algorithm-based method is proposed to extract the optimal number and placement of multi-RPs. The optimization approach and modeling framework are validated by comparing measured and simulated channel parameters. Results demonstrate that the proposed model effectively captures monostatic background channel characteristics, addresses a critical gap in ISAC channel modeling, and supports 6G standardization.
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