RF-URL: unsupervised representation learning for RF sensing

Ruiyuan Song, Dongheng Zhang, Zhi Wu, Cong Yu, Chunyang Xie, Shuai Yang, Yang Hu, Yan Chen

Abstract: The major obstacle for learning-based RF sensing is to obtain a high-quality large-scale annotated dataset. However, unlike visual datasets that can be easily annotated by human workers, RF signal is non-intuitive and non-interpretable, which causes the annotation of RF signals time-consuming and laborious. To resolve the rapacious appetite of annotated data, we propose a novel unsupervised representation learning (URL) framework for RF sensing, RF-URL, to learn a pre-training model on large-scale unannotated RF datasets that can be easily collected. RF-URL utilizes a contrastive framework to mind the gap between signal-processing-based RF sensing and learning-based RF sensing. By constructing positive and negative pairs through different signal processing representations, RF-URL seamlessly integrates the existing RF signal processing algorithms into the learning-based networks. Moreover, the RF-URL is carefully designed to take into account the asymmetric characteristics of different RF signal processing representations. We show that RF-URL is universal to a variety of RF sensing tasks by evaluating RF-URL in three typical RF sensing tasks (human gesture recognition, 3D pose estimation and silhouette generation) based on two general RF devices (WiFi and radar). All experimental results strongly demonstrate that RF-URL takes an important step towards learning-based solutions for large-scale RF sensing applications.