A Framework for Geometry-based Statistical Channel Modeling in ISAC Systems

Abstract

This paper proposes a comprehensive framework for a {geometry-based statistical model for} integrated sensing and communication (ISAC) tailored for bistatic systems. Our dual-component model decomposes the ISAC channel into a target channel encompassing all multipath components produced by a sensing target {parameterized} by the target's radar cross-section and scattering points, and a background channel comprising all other propagation paths that do not interact with the sensing target. The framework extends TR38.901 via a hybrid clustering approach, integrating spatiotemporally consistent deterministic clusters with stochastic clusters to preserve channel reciprocity and absolute delay alignment for sensing parameter estimation. Extensive simulations across {urban macro, urban micro, and indoor factory} scenarios demonstrate that the model maintains communication performance parity with the standard TR38.901, validated through bit-error rate analysis obtained via simulated and measured ISAC channels and channel capacity assessment, while enabling sensing performance evaluation, such as target ranging error for localization and receiver operating characteristic curves for detection probability.