AI-Enabled RF Sensing: A Pipeline-Centric Review from Design to Recognition

For electromagnetic (EM)-driven design, we summarize surrogate modeling and inverse design based on full-wave simulations and, when available, measurements, use adjacent-domain EM exemplars only as methodological templates where direct closed-loop RF design-to-recognition evidence remains limited, and explain why reported speedups depend on the sampled design domain, sample density near feasibility boundaries, and how constraint-boundary checks and final verification are implemented. For RF sensing and recognition, we discuss how the learned “signature” is shaped by the measurement chain (hardware, placement, synchronization, calibration, and preprocessing). We then outline an author-synthesized set of checkable evaluation and reporting items, including explicit domains/constraints, complete sensing metadata, cross-condition tests, and edge deployment evidence, where deployability is defined at the full-pipeline level and requires measured latency, throughput, peak memory, energy per inference, preprocessing overhead, runtime, numerical precision, and an explicit statement of whether communication or offloading is included.