Cross-environment generalization refers to the ability of a CSI-based Wi-Fi sensing model, trained in one or more specific physical environments, to maintain accurate performance when deployed in previously unseen environments with different spatial layouts, multipath propagation characteristics, and background interference conditions. This challenge arises because CSI measurements are highly sensitive to the specific geometry, furnishings, and reflective surfaces of a given space, causing models to learn environment-specific signal signatures rather than transferable representations of the target phenomenon — such as human activity or crowd count — leading to significant performance degradation when the deployment environment differs from the training environment. Key variants of this problem include cross-room generalization, where the model is tested in a different room of the same building type, cross-scenario generalization across functionally distinct environments such as laboratories versus bedrooms, and the related but distinct challenge of cross-device generalization, where changes in hardware placement or antenna configuration compound environment-induced distribution shift.
Source Papers
- CrossSense: Towards Cross-Site and Large-Scale WiFi Sensing ↗ — CrossSense: Towards Cross-Site and Large-Scale WiFi Sensing
- Deep Learning-Enhanced Human Sensing with Channel State Information: A Survey ↗ — Deep Learning-Enhanced Human Sensing with Channel State Info
- Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations ↗ — Exposing the CSI: A Systematic Investigation of CSI-based Wi
- Investigation of Environment Dependence in Wi-Fi CSI-Based Crowd Counting Systems ↗ — Investigation of Environment Dependence in Wi-Fi CSI-Based C