Radio maps are essential for electromagnetic spectrum awareness, supporting communication network optimization and spectrum management. However, existing methods often lack sufficient accuracy in complex propagation environments. To address this, we propose RMG-SRGAN, an improved radio map generation network based on an enhanced super-resolution generative adversarial network. The generator incorporates a Multi-scale Attention Aggregation (MAA) module that strengthens feature representation using multi-scale fusion and dual-path attention in spatial and channel dimensions. The discriminator includes a Feature Enhancement (FE) module to boost discriminative power through multi-stage feature processing.In our evaluation, we prioritize physical fidelity and structural reliability over generic perceptual metrics. Consequently, we employ Root Mean Square Error (RMSE) to quantify the precision of predicted signal strength and the F1-Score to assess the classification accuracy of coverage zones versus blind spots. Extensive experiments on the RadioMapSeer dataset demonstrate that RMG-SRGAN achieves state-of-the-art performance, securing the lowest RMSE and highest F1-Score compared to existing baselines.
Gao et al. (Wed,) studied this question.