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In recent years, (Radio Frequency) RF sensing has gained increasing popularity due to its pervasiveness, low-cost, non-intrusiveness, and privacy preservation. However, realizing the promises of RF sensing is highly non-trivial, given typical challenges such as multipath and interference. One potential solution leverages deep learning to build direct mappings from RF domain to target domains, hence avoiding complex RF physical modeling. While earlier solutions exploit only simple feature extraction and classification modules, an emerging trend adds functional layers on top of elementary modules for more powerful generalizability and flexible applicability. To better understand this potential, this paper takes a layered approach to summarize RF sensing enabled by deep learning. Essentially, we present a four-layer framework: physical, backbone, generalization, and application. While this layered framework provides readers a systematic methodology for designing deep interpreted RF sensing, it also facilitates us to make improvement proposals and to hint future research opportunities.

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