Enhancing RF Sensing with Deep Learning: A Layered Approach

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 is highly nontrivial, given typical challenges such as multipath interference. One potential solution leverages deep learning build direct mappings from domain target domains, hence avoiding complex physical modeling. While earlier solutions exploit only simple feature extraction classification modules, an emerging trend adds functional layers on top elementary modules for more powerful generalizability flexible applicability. To better understand this potential, article takes a layered approach summarize enabled by learning. Essentially, we present four-layer framework: physical, backbone, generalization, application. framework provides readers systematic methodology designing interpreted sensing, it also facilitates making improvement proposals hints at future research opportunities.