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The past years have witnessed the rapid conceptualization and development of wireless sensing based on Channel State Information (CSI) with commodity WiFi devices. Recent studies have demonstrated the vast potential of WiFi sensing in detection, recognition, and estimation applications. However, the widespread deployment of WiFi sensing systems still faces a significant challenge: how to ensure the sensing performance when exposing a pre-trained sensing system to new domains, such as new environments, different configurations, and unseen users, without data collection and system retraining. This survey provides a comprehensive review of recent research efforts on cross-domain WiFi Sensing. We first introduce the mathematical model of CSI and explore the impact of different domains on CSI. Then we present a general workflow of cross-domain WiFi sensing systems, which consists of signal processing and cross-domain sensing. Five cross-domain sensing algorithms, including domain-invariant feature extraction, virtual sample generation, transfer learning, few-shot learning and big data solution, are summarized to show how they achieve high sensing accuracy when encountering new domains. The advantages and limitations of each algorithm are also summarized and the performance comparison is made based on different applications. Finally, we discuss the remaining challenges to further promote the practical usability of cross-domain WiFi sensing systems.
Chen et al. (Wed,) studied this question.