ACT-GAN: Radio map construction based on generative adversarial networks with ACT blocks

The radio map serves as a vital tool in assessing wireless communication networks and monitoring radio coverage, providing a visual representation of electromagnetic spatial characteristics. To address the limitation of low accuracy in current radio map construction method, this article presents a novel method based on Generative Adversarial Network (GAN), called ACT-GAN. This method incorporates the aggregated contextual-transformation block, the convolutional block attention module, and the transposed convolutional block into the generator, significantly enhancing the construction accuracy of radio map. The performance of ACT-GAN is validated in three distinct scenarios. The results indicate that, in scenario 1, where the transmitter locations are known, the average reduction in Root Mean Square Error (RMSE) is 14.6%. In scenario 2, where the transmitter locations are known and supplemented with sparse measurement maps, the average reduction in RMSE is 13.3%. Finally, in scenario 3, where the transmitter locations are unknown, the average reduction in RMSE is 7.1%. Moreover, the proposed model exhibits clearer predictive results and can accurately capture multi-scale shadow fading.