arXiv Machine Learning By Maciej Cicho\'n, Patrycja Go\l{}a\'s, Maksymilian Bartosik, Angelika Walczak, Agata Bielecka-D\k{a}browa, Maciej \'Slot

When Magnitude Suffices: Reconstructing Time-Domain-Gated Microwave Signals Without Phase on a Thoracic Phantom

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The study demonstrates that time‑domain‑gated microwave signals can be reconstructed from magnitude‑only measurements using a spectrum analyzer, eliminating the need for phase data typically obtained with a vector network analyzer. By selecting appropriate gate centers, applying per‑bin reference corrections, and training a fully connected neural network on paired sweeps, the authors achieve reconstruction errors comparable to VNA‑based methods for both reflection ($S_{11}$) and transmission ($S_{21}$) measurements on a thoracic phantom with multiple dielectric states. The approach shows that when magnitude sufficiently encodes phase information—particularly in reflection—scalar instruments can provide gated responses without phase measurement, and it outperforms linear mapping on an open breast phantom dataset.

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