RadioPiT: Radio Map Generation with Pixel Transformer Driven by Ultra-Sparse Real-World Data

As wireless communication networks rapidly evolve, spectrum resources are increasingly scarce, making effective spectrum management critically important. Radio map is a spatial representation of signal characteristics across different locations in a given area, which serves as a key tool for enabling precise spectrum management. To generate accurate radio maps, extensive research efforts have been made. However, most existing studies are conducted on simulation data, which differs significantly from real-world data and cannot accurately reflect the spectrum characteristics of practical environments. To tackle this problem, we construct a dataset of real-world radio map with a self-developed measurement system. Due to the limited volume of real-world data and the distributional discrepancies between simulation and real-world data, we propose a Pixel Transformer (PiT)- based model enhanced with the test-time adaptation (TTA) strategy, named RadioPiT, for real-world radio map generation. Experimental results demonstrate that our proposed RadioPiT significantly outperforms baseline methods in real-world scenarios, yielding a 21.9% decrement in the root mean square error (RMSE) compared to RadioUNet.