Multi-Station WiFi CSI Sensing Framework Robust to Station-wise Feature Missingness and Limited Labeled Data

Abstract

We propose a WiFi Channel State Information (CSI) sensing framework for multi-station deployments that addresses two fundamental challenges in practical CSI sensing: station-wise feature missingness and limited labeled data. Feature missingness is commonly handled by resampling unevenly spaced CSI measurements or by reconstructing missing samples, while label scarcity is mitigated by data augmentation or self-supervised representation learning. However, these techniques are typically developed in isolation and do not jointly address long-term, structured station unavailability together with label scarcity. To bridge this gap, we explicitly incorporate station unavailability into both representation learning and downstream model training. Specifically, we adapt cross-modal self-supervised learning (CroSSL), a representation learning framework originally designed for time-series sensory data, to multi-station CSI sensing in order to learn representations that are inherently invariant to station-wise feature missingness from unlabeled data. Furthermore, we introduce Station-wise Masking Augmentation (SMA) during downstream model training, which exposes the model to realistic station unavailability patterns under limited labeled data. Our experiments show that neither missingness-invariant pre-training nor station-wise augmentation alone is sufficient; their combination is essential to achieve robust performance under both station-wise feature missingness and label scarcity. The proposed framework provides a practical and robust foundation for multi-station WiFi CSI sensing in real-world deployments.

Figures (14)

• Figure 1: Multi-stations CSI sensing setup and visualization of timestamp alignment. Multiple stations transmit frames to the AP. A camera provides images for ground-truth of downstream model training. As illustrated in the upper timeline, WiFi frames from different stations and camera images are acquired asynchronously and at heterogeneous intervals.
• Figure 2: Illustration of labeled and unlabeled sample generation from asynchronous CSI and label streams. Both labeled and unlabeled samples are constructed by applying a fixed-length window around a reference timestamps. Due to asynchronous sensing and heterogeneous transmission intervals, some windows may contain no CSI from particular stations, resulting in station-wise feature missingness.
• Figure 3: Overview of the proposed framework. The framework learns station-missingness-invariant representations from multi-station CSI and applies them to downstream tasks. CroSSL is used to learn robust global embeddings from unlabeled CSI by masking intermediate station embeddings during representation learning. For downstream model training, SMA masks entire station inputs to simulate realistic station unavailability. The same encoders and aggregator are shared across both phases, enabling robust inference under station-wise feature missingness and limited labeled data.
• Figure 4: Experimental setup of office-like environment.
• Figure 5: Examples of one-dimensional position estimation. The white line denotes the ground-truth label, while the blue, green, and red lines indicate the predictions obtained with 1, 4, and 8 stations, respectively. $\mathrm{RMSE}_{x\,\mathrm{stations}}$ represents the RMSE of using $x$ stations.