  Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless
communications for tasks such as spectrum management and network planning.
However, traditional machine-learning-based MB-RMR methods, which rely heavily
on simulated data or complete structured ground truth, face significant
deployment challenges. These challenges stem from the differences between
simulated and actual data, as well as the scarcity of real-world measurements.
To address these challenges, our study presents RadioGAT, a novel framework
based on Graph Attention Network (GAT) tailored for MB-RMR within a single
area, eliminating the need for multi-region datasets. RadioGAT innovatively
merges model-based spatial-spectral correlation encoding with data-driven
radiomap generalization, thus minimizing the reliance on extensive data
sources. The framework begins by transforming sparse multi-band data into a
graph structure through an innovative encoding strategy that leverages radio
propagation models to capture the spatial-spectral correlation inherent in the
data. This graph-based representation not only simplifies data handling but
also enables tailored label sampling during training, significantly enhancing
the framework's adaptability for deployment. Subsequently, The GAT is employed
to generalize the radiomap information across various frequency bands.
Extensive experiments using raytracing datasets based on real-world
environments have demonstrated RadioGAT's enhanced accuracy in supervised
learning settings and its robustness in semi-supervised scenarios. These
results underscore RadioGAT's effectiveness and practicality for MB-RMR in
environments with limited data availability.
