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