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

TL;DR

RadioPiT introduces a novel Pixel Transformer-based model with test-time adaptation for generating accurate real-world radio maps from sparse data, outperforming existing methods.

Contribution

The paper presents RadioPiT, a new model that effectively generates real-world radio maps using limited data and a self-developed measurement system, addressing simulation-to-reality gaps.

Findings

RadioPiT reduces RMSE by 21.9% compared to RadioUNet.

The model outperforms baseline methods in real-world radio map generation.

Experimental results validate the effectiveness of Pixel Transformer and TTA strategies.

Abstract

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.