Domain-Factored Untrained Deep Prior for Spectrum Cartography
Subash Timilsina, Sagar Shrestha, Lei Cheng, Xiao Fu · IEEE Signal Processing Letters · 2025
Spectrum cartography(SC) aims to estimate the radio power map of multiple emitters over space and frequency using limited sensor data. Recent advances leverage learneddeep generative models(DGMs) as structural priors, achieving state-of-the-art performance by capturing complex spatial-spectral patterns. However, DGMs require large training datasets and may suffer under distribution shifts. To address these limitations, we propose atraining-freeSC approach based onuntrained neural networks(UNNs), which encode structural priors through architectural design. Our custom UNN exploits a spatio-spectral factorization model rooted in the physical structure of radio maps, enabling low sample complexity. Experiments show that our method matches the performance of DGM-based SC without any training data.