ISAC Channel Measurements and Closed-Loop Modeling With Bidirectional Parameter Coupling in Industrial IoT Scenarios

With the advent of 6G networks, integrated sensing and communication (ISAC) is a key enabling technology for native perception in complex industrial Internet of Things (IoT) environments, which demands accurate ISAC channel models. However, most existing models mainly capture correlation at the cluster level, making it difficult to characterize finer-grained parameter relationships and intra-cluster variations, and lack an effective mechanism for bidirectional parameter coupling between communication and sensing channels in industrial IoT scenarios. This paper proposes a shared-cluster geometry-based stochastic ISAC (SC-GBSM-ISAC) channel model for industrial IoT. The model is parameterized using channel measurements at 38 GHz and 132 GHz in a high-clutter indoor factory (InF (A)) scenario. Compared with existing ISAC channel models, the proposed model has the following features. First, a closed-loop “sensing–communication–sensing” channel modeling framework is constructed based on 3GPP TR 38.901, which integrates bidirectional parameter interaction between communication and sensing channels. Second, by selectively reusing sensing-target clusters to construct shared clusters for communication-channel modeling, the model introduces a physically consistent shared-scattering structure while preserving the statistical characteristics specified in 3GPP TR 38.901. Third, by combining scatterer information inferred from the communication channel with a scenario-dependent sensing probability model, the sensing background component is reconstructed. Finally, measurement-simulation comparisons in an independent low-clutter InF (B) scenario demonstrate that the proposed model effectively reproduces bidirectional parameter coupling and primary spatio-temporal statistical metrics at 38/132 GHz, validating its accuracy and cross-scenario consistency in the measured scenarios.