As a novel technology in the sixth-generation (6G) wireless communication systems, integrated sensing and communication (ISAC) enables intelligent agents to perceive, predict, and interact with the environment. It is crucial for ISAC to acquire environmental information based on electromagnetic propagation, referred to as channel semantics, to facilitate tasks such as decision-making and beamforming. However, channel models that focus on physical characteristics face challenges in representing the semantics embedded in the channel, thereby limiting the performance evaluation of ISAC systems. To tackle this, we present a novel unified framework for channel modeling from the conceptual event perspective. By leveraging a multi-level semantic structure and characterized knowledge libraries, the framework decomposes complex channel characteristics into composable and extensible high-level semantic characterization, thereby better capturing the relationship between the environment and channel, and enabling more flexible adjustments of channel models for different events without requiring a complete reset. Specifically, we define channel semantics from three levels: status semantics, behavior semantics, and event semantics, corresponding to channel transient multipaths, channel time-varying trajectories, and channel topology, respectively. Taking a realistic vehicular ISAC scenario as an example, we perform semantic clustering through depth estimation and semantic segmentation of environmental images, and further characterize the channel status semantics by fitting multipath statistical distributions; behavior semantics are modeled using Markov chains to capture time-varying characteristics; event semantics are characterized by employing a co-occurrence matrix. The results indicate that the proposed model can generate accurate channels whereas representing rich semantic information. Additionally, generalization of the model for customized semantics is demonstrated.

Ray tracing (RT) is a key tool for establishing accurate mappings between physical environments and propagation channels. However, due to the complexity of indoor scatterers and the difficulty of fully capturing modeling details, there is no unified specification for scenario modeling, leaving the impact of modeling detail on RT simulations unclear. This work proposes an indoor modeling method based on LiDAR point clouds, which achieves automated mesh reconstruction via minimum bounding box estimation and generates scenario models at different levels of detail (LOD). Comparative analysis shows that modeling detail significantly affects multipath components (MPCs) accuracy. A simple-detail model fails to accurately capture MPCs, whereas a medium-detail model exhibits higher simulation accuracy, and the full-detail model achieves the closest agreement with the ground truth model, although further accuracy gains diminish as complexity increases. Furthermore, indoor scatterers detail has a greater influence on RT results than room boundaries. To balance simulation accuracy and computational complexity, indoor scatterers should include at least external contour features, with opening structures preferred. In contrast, RB modeling can be simplified to a medium-detail model with only external contours. The sensitivity of different channel parameters to LOD also depends on the propagation scenario: in LOS scenarios, DS and PL are highly sensitive to LOD, whereas AS is relatively insensitive; in NLOS scenarios, DS and AS are sensitive, while PL sensitivity significantly decreases.

Integrated Sensing and Communication (ISAC) is a promising technology for 6G, with the goal of providing end-to-end information processing and inherent perception capabilities for future communication systems. Within ISAC emerging application scenarios, vehicular ISAC technologies have the potential to enhance traffic efficiency and safety through integration of communication and synchronized perception abilities. To establish a foundational theoretical support for vehicular ISAC system design and standardization, it is necessary to conduct channel measurements, and model to obtain a deep understanding of the radio propagation. In this paper, a dynamic statistical channel model is proposed for vehicular ISAC scenarios, incorporating Sensing Multi-Path Components (S-MPCs) and Clutter Multi-Path Components (C-MPCs), which are identified by the proposed tracking algorithm. Based on actual vehicular ISAC channel measurements at 28 GHz, time-varying sensing characteristics in front, left, and right directions are investigated. To model the dynamic evolution process of channel, number of new S-MPCs, lifetimes, initial power and delay positions, dynamic variations within their lifetimes, clustering, power decay, and fading of C-MPCs are statistically characterized. Finally, the paper provides implementation of dynamic vehicular ISAC model and validates it by comparing key simulation statistics between measurements and simulations.

Recently, deep learning enabled semantic communications have been developed to understand transmission content from semantic level, which realize effective and accurate information transfer. Aiming to the vision of sixth generation (6G) networks, wireless devices are expected to have native perception and intelligent capabilities, which associate wireless channel with surrounding environments from physical propagation dimension to semantic information dimension. Inspired by these, we aim to provide a new paradigm on wireless channel from semantic level. A channel semantic model and its characterization framework are proposed in this paper. Specifically, a channel semantic model composes of status semantics, behavior semantics and event semantics. Based on actual channel measurement at 28 GHz, as well as multi-mode data, example results of channel semantic characterization are provided and analyzed, which exhibits reasonable and interpretable semantic information.