While decade-long research has clearly demonstrated the vast potential of radio frequency (RF) for many human sensing tasks, scaling this technology to large scenarios remained problematic with conventional approaches. Recently, researchers have successfully applied deep learning to take radio-based sensing to a new level. Many different types of deep learning models have been proposed to achieve high sensing accuracy over a large population and activity set, as well as in unseen environments. Deep learning has also enabled detection of novel human sensing phenomena that were previously not possible. In this survey, we provide a comprehensive review and taxonomy of recent research efforts on deep learning based RF sensing. We also identify and compare several publicly released labeled RF sensing datasets that can facilitate such deep learning research. Finally, we summarize the lessons learned and discuss the current limitations and future directions of deep learning based RF sensing.

中文翻译：

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基于无线电的人类感知的深度学习：最新进展和未来方向

尽管长达十年的研究清楚地表明了射频（RF）在许多人类感知任务中的巨大潜力，但是将这种技术扩展到大型场景仍然是传统方法所面临的问题。最近，研究人员成功地应用了深度学习，将基于无线电的传感技术提升到了一个新的水平。已经提出了许多不同类型的深度学习模型，以在较大的人口和活动集以及看不见的环境中实现较高的传感精度。深度学习还可以检测以前不可能的新型人类感知现象。在本次调查中，我们提供了有关基于深度学习的RF感应的最新研究成果的全面综述和分类。我们还确定并比较了几个公开发布的，带有标签的RF感应数据集，这些数据集可以促进此类深度学习研究。最后，我们总结总结的经验教训，并讨论基于深度学习的RF传感的当前局限性和未来发展方向。