WiFi sensing has been evolving rapidly in recent years. Empowered by propagation models and deep learning methods, many challenging applications are realized such as WiFi-based human activity recognition and gesture recognition. However, in contrast to deep learning for visual recognition and natural language processing, no sufficiently comprehensive public benchmark exists. In this paper, we review the recent progress on deep learning enabled WiFi sensing, and then propose a benchmark, SenseFi, to study the effectiveness of various deep learning models for WiFi sensing. These advanced models are compared in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, feature transferability, and adaptability of unsupervised learning. It is also regarded as a tutorial for deep learning based WiFi sensing, starting from CSI hardware platform to sensing algorithms. The extensive experiments provide us with experiences in deep model design, learning strategy skills and training techniques for real-world applications. To the best of our knowledge, this is the first benchmark with an open-source library for deep learning in WiFi sensing research. The benchmark codes are available at https://github.com/xyanchen/WiFi-CSI-Sensing-Benchmark.

翻译：WiFi感知技术近年来发展迅速。借助传播模型与深度学习方法，已实现了诸多具有挑战性的应用，如基于WiFi的人体活动识别和手势识别。然而，不同于计算机视觉和自然语言处理领域中的深度学习研究，该领域尚缺乏足够全面的公开基准。本文回顾了深度学习赋能WiFi感知的最新进展，并提出基准SenseFi，用以研究各类深度学习模型在WiFi感知中的有效性。我们从感知任务类型、WiFi平台、识别准确率、模型大小、计算复杂度、特征迁移性以及无监督学习的适应性等维度对这些先进模型进行了比较。本工作亦可视为基于深度学习的WiFi感知教程，涵盖从CSI硬件平台到感知算法的完整流程。通过大量实验，我们积累了面向真实应用的深度模型设计、学习策略技巧及训练技术的经验。据我们所知，这是首个面向WiFi感知研究且提供开源代码库的深度学习基准。基准代码详见：https://github.com/xyanchen/WiFi-CSI-Sensing-Benchmark。