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Context-Aware Predictive Coding: A Representation Learning Framework for WiFi Sensing
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DOI：10.1109/OJCOMS.2024.3465216.png)
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WiFi sensing is an emerging technology that utilizes wireless signals for various sensing applications. However, the reliance on supervised learning and the scarcity of labelled data and the incomprehensible channel state information (CSI) data pose significant challenges. These issues affect deep learning models' performance and generalization across different environments. Consequently, self-supervised learning (SSL) is emerging as a promising strategy to extract meaningful data representations with minimal reliance on labelled samples. In this paper, we introduce a novel SSL framework called Context-Aware Predictive Coding (CAPC), which effectively learns from unlabelled data and adapts to diverse environments. CAPC integrates elements of Contrastive Predictive Coding (CPC) and the augmentation-based SSL method, Barlow Twins, promoting temporal and contextual consistency in data representations. This hybrid approach captures essential temporal information in CSI, crucial for tasks like human activity recognition (HAR), and ensures robustness against data distortions. Additionally, we propose a unique augmentation, employing both uplink and downlink CSI to isolate free space propagation effects and minimize the impact of electronic distortions of the transceiver. Our evaluations demonstrate that CAPC not only outperforms other SSL methods and supervised approaches, but also achieves superior generalization capabilities. Specifically, CAPC requires fewer labelled samples while significantly outperforming supervised learning by an average margin of 30.53% and surpassing SSL baselines by 6.5% on average in low-labelled data scenarios. Furthermore, our transfer learning studies on an unseen dataset with a different HAR task and environment showcase an accuracy improvement of 1.8% over other SSL baselines and 24.7% over supervised learning, emphasizing its exceptional cross-domain adaptability. These results mark a significant breakthrough in SSL applications for WiFi sensing, highlighting CAPC's environmental adaptability and reduced dependency on labelled data.
Keyword:
Sensors
Wireless fidelity
Wireless sensor networks
Wireless communication
Data models
Human activity recognition
Supervised learning
Channel state information
self-supervised learning
representation learning
WiFi sensing
human activity recognition
期刊
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4.3
论文数:
1.7K
被引数:
991
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