A Radio Map Inpainting Construction Method for Dynamic Scenarios

This paper proposes a dynamic radio map (DRM) construction method based on a generative adversarial network (GAN) and AOT attention mechanism to address the increasing demand for radio spectrum resources and the difficulty of existing spectrum allocation methods to meet diversified needs. The method utilizes existing static radio maps (SRMs) and a priori information (e.g., transmitting source parameters, building information, and vehicle locations), generates masks through the path occlusion matrix and adds them to the SRMs, and repairs only the masked areas to complete the construction of the DRMs. The AOT attentional mechanism enhances the network's capability of capturing global information and local texture features of the radio maps by setting up sub-kernels with different expansion rates. Ability. The experiments are conducted on the RadioMapSeer dataset. The results show that the proposed AOTGAN algorithm outperforms other comparative algorithms in terms of Root Mean Square Error (RMSE), Normalized Mean Square Error (NMSE), and Structural Similarity (SSIM) and can efficiently capture the dynamic vehicle factors and recover the real DRM.