A General Channel Model for Integrated Sensing and Communication Scenarios

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.

A Non-Stationary Channel Model with Correlated NLoS/LoS States for ELAA-mMIMO

“…For deterministic models, techniques like ray tracing or geometric optics are used to realistically simulate the NF-channel characteristics. However, their reliance on detailed calculations and intricate path tracing makes them computationally demanding, especially for ELAA systems with a large number of antennas [5]. In GBSMs, clusters are depicted as fundamental units, each representing a set of reflections with similar characteristics.…”

“…For each cluster, the channel coefficients are randomly generated based on a certain statistical distribution. GBSMs demonstrate remarkable adaptability to ISAC applications through the innovative concept of sensing clusters [5]. Despite their advantages, GBSMs face challenges in their dependence on geometric information, limited suitability, and generality for link-level simulations [4].…”

“…However, the more general form of ELAA is represented by uniform rectangular array (URA), which can used in both RIS and XL-MIMO, and it requires a three-dimensional (3D) channel model [2], [12]. In addition, sensing capabilities stand out as a crucial requirement for ISAC in 6G [2], [5]. This highlights the critical importance of modeling general fading channels for ELAA to advance communications and ISAC applications future mobile networks.…”

Dual-Pulse Repeated Frequency Waveform Design of Time-Division Integrated Sensing and Communication Based on a 5G New Radio Communication System

“…Over the past several years, integrated sensing and communications (ISAC) has attracted widespread attention. ISAC can reduce hardware costs and greatly improve frequency spectrum efficiency by reusing hardware and the same frequency spectrum [1][2][3]. As popularly seen in the literature, ISAC has broad applications, such as autonomous driving vehicles [4], vehicle-to-vehicle [5] systems, indoor locations [6], wireless radar sensor networks [7], and drone surveillance networks [8].…”

Transforming Fading Channel From Fast to Slow: Intelligent Refracting Surface Aided High-Mobility Communication

“…The lack of sensory information results in the fact that the transceiver cannot detect dynamic objects and static objects. In this case, it is significantly difficult to distinguish dynamic scatterers and static scatterers, which are certain objects, e.g., dynamic vehicles or static buildings/trees, in the physical environment [8]- [10]. Due to the inability to distinguish dynamic and static scatterers, the vehicular channel characteristics under different vehicular traffic density (VTD) conditions cannot be explored.…”