Learning to sense: Deep learning for wireless sensing with less training efforts

Wireless sensing is an emerging technique which empowers wireless devices with additional sensing ability, that is, the ability to sense the target location, activity, gesture, vital signs, etc., in a device-free manner by analyzing the influence of the target on surrounding wireless signals. Benefiting from its excellent feature extraction and analysis capability, deep learning has emerged as a promising tool to realize wireless sensing. However, the labor intensive training efforts on collecting training samples or retraining a trained system limit its practical application. This article attempts to provide an integrated view on this field and discuss the feasibility of leveraging some new types of deep learning networks to accomplish wireless sensing tasks with less training efforts. Specifically, we first introduce the deep learning based wireless sensing framework, review the methods to construct radio images, and summarize different types of deep learning networks. Then, we focus on new types of deep learning networks which could reduce training efforts from two perspectives, that is, alleviating the efforts of retraining the system in a new scenario by learning a universal similarity evaluation ability utilizing a deep similarity evaluation networks, and reducing the efforts of collecting samples by generating virtual training samples using deep generative adversarial networks. Then, we elaborate existing challenges and forecast future research directions. Finally, we carry out experiments to evaluate the effectiveness of the introduced schemes using wireless gesture recognition as the case study.