# Context-Aware Predictive Coding: A Representation Learning Framework for WiFi Sensing

arXiv: https://arxiv.org/abs/2410.01825 · HTML: https://mlsanity.com/paper/2410.01825/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 69 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [Understanding Dimensional Collapse in Contrastive Self-supervised Learning](https://mlsanity.com/paper/2110.09348/) (2021)
- [Unsupervised Scalable Representation Learning for Multivariate Time Series](https://mlsanity.com/paper/1901.10738/) (2019)
- [Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods](https://mlsanity.com/paper/2205.11508/) (2022)
- [MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing](https://mlsanity.com/paper/2305.10345/) (2023)
- [Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification](https://mlsanity.com/paper/2208.06616/) (2022)
- [SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing](https://mlsanity.com/paper/2207.07859/) (2022)
- [STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks](https://mlsanity.com/paper/1902.07849/) (2019)
- [WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing](https://mlsanity.com/paper/2402.09430/) (2024)
- [AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning](https://mlsanity.com/paper/2205.01629/) (2022)
- [SiFall: Practical Online Fall Detection with RF Sensing](https://mlsanity.com/paper/2301.03773/) (2023)
- [GaitFi: Robust Device-Free Human Identification via WiFi and Vision Multimodal Learning](https://mlsanity.com/paper/2208.14326/) (2022)
- [Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning](https://mlsanity.com/paper/2207.03046/) (2022)
- [FDD Massive MIMO Uplink and Downlink Channel Reciprocity Properties: Full or Partial Reciprocity?](https://mlsanity.com/paper/1912.11221/) (2019)
- [Self-Supervised WiFi-Based Activity Recognition](https://mlsanity.com/paper/2104.09072/) (2021)
- [RSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing](https://mlsanity.com/paper/2402.04888/) (2024)
- [Diffusion Model-based Contrastive Learning for Human Activity Recognition](https://mlsanity.com/paper/2408.05567/) (2024)
- [MaskFi: Unsupervised Learning of WiFi and Vision Representations for Multimodal Human Activity Recognition](https://mlsanity.com/paper/2402.19258/) (2024)
- [BTS: Bifold Teacher-Student in Semi-Supervised Learning for Indoor Two-Room Presence Detection Under Time-Varying CSI](https://mlsanity.com/paper/2212.10802/) (2022)

## Built on by (most cited first, 4 of 4)

- [Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2508.15185/) (2025)
- [CIG-MAE: Cross-Modal Information-Guided Masked Autoencoder for Self-Supervised WiFi Sensing](https://mlsanity.com/paper/2512.04723/) (2025)
- [MU-SHOT-Fi: Self-Supervised Multi-User Wi-Fi Sensing with Source-free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2605.01369/) (2026)
- [AMAR: Lightweight Attention-Based Multi-User Activity Recognition from Wi-Fi CSI](https://mlsanity.com/paper/2605.20649/) (2026)

## Abstract (arXiv)

WiFi sensing is an emerging technology that utilizes wireless signals for various sensing applications. However, the reliance on supervised learning, the scarcity of labelled data, and the incomprehensible channel state information (CSI) 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 and surpassing SSL baselines. Furthermore, our transfer learning studies on an unseen dataset with a different HAR task and environment showcase an accuracy improvement of 1.8 percent over other SSL baselines and 24.7 percent over supervised learning, emphasizing its exceptional cross-domain adaptability.