A Cluster-Based Statistical Channel Model for Integrated Sensing and Communication Channels

TL;DR

This paper introduces a novel cluster-based statistical channel model for integrated sensing and communication (ISAC) in 6G, capturing correlation and unique features of sensing channels based on 28 GHz measurements.

Contribution

It proposes a new framework combining communication and sensing clusters, including special structures, to accurately model ISAC channels and their correlation.

Findings

Model validated with measurements and simulations

Includes novel sensing cluster structures like shared and newborn clusters

Enhances realism of ISAC channel modeling for 6G applications

Abstract

The emerging 6G network envisions integrated sensing and communication (ISAC) as a promising solution to meet growing demand for native perception ability. To optimize and evaluate ISAC systems and techniques, it is crucial to have an accurate and realistic wireless channel model. However, some important features of ISAC channels have not been well characterized, for example, most existing ISAC channel models consider communication channels and sensing channels independently, whereas ignoring correlation under the consistent environment. Moreover, sensing channels have not been well modeled in the existing standard-level channel models. Therefore, in order to better model ISAC channel, a cluster-based statistical channel model is proposed in this paper, which is based on measurements conducted at 28 GHz. In the proposed model, a new framework based on 3GPP standard is proposed, which includes communication clusters and sensing clusters. Clustering and tracking algorithms are used to extract and analyze ISAC channel characteristics. Furthermore, some special sensing cluster structures such as shared sensing cluster, newborn sensing cluster, etc., are defined to model correlation and difference between communication and sensing channels. Finally, accuracy of the proposed model is validated based on measurements and simulations.