Generative AI-Empowered Semantic Twin Channel Model for ISAC

TL;DR

This paper introduces a semantics-oriented channel model for ISAC that leverages generative AI to produce realistic, controllable channel realizations based on environmental semantics, bridging the gap between sensing and communication models.

Contribution

It proposes a unifying semantic channel modeling principle and develops a generative AI-powered semantic twin channel model for realistic, scalable ISAC channel simulation.

Findings

Semantic consistency maintained under multi-view settings

Generative model produces physically plausible channel realizations

Facilitates controllable simulation and dataset generation

Abstract

Integrated sensing and communication (ISAC) increasingly exposes a gap in today's channel modeling. Efficient statistical models focus on coarse communication-centric metrics, and therefore miss the weak but critical multipath signatures for sensing, whereas deterministic models are computationally inefficient to scale for system-level ISAC evaluation. This gap calls for a unifying abstraction that can couple what the environment means for sensing with how the channel behaves for communication, namely, environmental semantics. This article clarifies the meaning and essentiality of environmental semantics in ISAC channel modeling and establishes how semantics is connected to observable channel structures across multiple semantic levels. Based on this perspective, a semantics-oriented channel modeling principle was advocated, which preserves environmental semantics while abstracting unnecessary detail to balance accuracy and complexity. Then, a generative AI-empowered semantic twin channel model (STCM) was introduced to generate a family of physically plausible channel realizations representative of a semantic condition. Case studies further show semantic consistency under challenging multi-view settings, suggesting a practical path to controllable simulation, dataset generation, and reproducible ISAC benchmarking toward future design and standardization.