Channel Modeling Framework for Both Communications and Bistatic Sensing Under 3GPP Standard

TL;DR

This paper introduces a bistatic ISAC channel modeling framework compatible with 3GPP standards, enabling improved sensing and communication evaluation through enhanced cluster generation, target modeling, and reflection techniques.

Contribution

It extends the 3GPP channel model to support bistatic sensing, adding features for target characterization and reflection modeling, validated by simulations and experiments.

Findings

Framework is compatible with 3GPP communication models.

Enhanced cluster generation supports sensing targets.

Validation through ray tracing and experimental studies.

Abstract

Integrated sensing and communications (ISAC) is considered a promising technology in the B5G/6G networks. The channel model is essential for an ISAC system to evaluate the communication and sensing performance. Most existing channel modeling studies focus on the monostatic ISAC channel. In this paper, the channel modeling framework for bistatic ISAC is considered. The proposed channel modeling framework extends the current 3GPP channel modeling framework and ensures the compatibility with the communication channel model. To support the bistatic sensing function, several key features for sensing are added. First, more clusters with weaker power are generated and retained to characterize the potential sensing targets. Second, the target model can be either deterministic or statistical, based on different sensing scenarios. Furthermore, for the statistical case, different reflection models are employed in the generation of rays, taking into account spatial coherence. The effectiveness of the proposed bistatic ISAC channel model framework is validated by both ray tracing simulations and experiment studies. The compatibility with the 3GPP communication channel model and how to use this framework for sensing evaluation are also demonstrated.