A Novel Multi-Reference-Point Modeling Framework for Monostatic Background Channel: Toward 3GPP ISAC Standardization

Journal article, 2026

Integrated Sensing and Communication (ISAC) has been identified as a key 6 G 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. However, existing geometry-based stochastic channel models (GBSMs) are developed for propagation between spatially separated transmitter (Tx) and receiver (Rx), and are therefore not directly applicable to the monostatic background channel, where the Tx and Rx are co-located. In this paper, we firstly conduct ISAC monostatic background channel measurements in an indoor scenario at 28 GHz. Realistic channel parameters are extracted, revealing pronounced single-hop propagation and discrete multipath distribution. Inspired by these properties, the concept of the Reference Point (RP), i.e. the virtual spatially separated Rx, is introduced and a novel multi-RP based modeling framework is proposed, where the monostatic background channel is described as the superposition of sub-channels between the monostatic Tx and RPs. This model restores a tractable geometric representation while remaining compatible with the standardized GBSM framework. Finally, we develop a 3GPP-extended implementation framework and propose a genetic algorithm-based method to determine the optimal number and placement of 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 6 G standardization.