Geometry-Based Stochastic MIMO Channel Model for Near-Field and Far-Field Scenarios of Integrated Sensing and Communications

Abstract

Integrated sensing and communication (ISAC) has become a promising technology in current sixth-generation (6G) wireless communications due to its ability to support both communication and sensing. Existing works regarding ISAC channels mainly focus on far-field propagation conditions based on plane wave assumption. However, with the deployment of ultra-massive multiple-in multiple-out (MIMO), the far-field assumption may no longer hold, as the communication and sensing propagation distances can become comparable to the antenna size. To address this issue, we propose a three-dimensional MIMO channel model that captures the characteristics of communication and sensing channels in ISAC environments, considering near-field propagation conditions. Additionally, we distinguish the communication and sensing propagation environments by employing different effective scatterer distributions. In the model, the sensing channel is divided into target sensing and environmental sensing components, whereas the communication channel is divided into line-of-sight and non-line-of-sight components. The weighted sum of each component in communication and sensing channels results in accurate channel representations. Based on the proposed model, we derive and thoroughly investigate space-time-frequency correlation function, normalized absolute error function, and channel capacity. A key observation is that the correlation between communication and sensing channels is high in dense scatterer environments, which implies that the communication channel can be recovered from the sensing channel. Error function results indicate that the proposed channel model achieves fairly high accuracy in ultra-massive MIMO scenarios. These findings provide essential support for the development of ISAC systems in future 6G wireless communications.