  The radio map, serving as a visual representation of electromagnetic spatial
characteristics, plays a pivotal role in assessment of wireless communication
networks and radio monitoring coverage. Addressing the issue of low accuracy
existing in the current radio map construction, this paper presents a novel
radio map construction method based on generative adversarial network (GAN) in
which the Aggregated Contextual-Transformation (AOT) block, Convolutional Block
Attention Module (CBAM), and Transposed Convolution (T-Conv) block are applied
to the generator, and we name it as ACT-GAN. It significantly improves the
reconstruction accuracy and local texture of the radio maps. The performance of
ACT-GAN across three different scenarios is demonstrated. Experiment results
reveal that in the scenario without sparse discrete observations, the proposed
method reduces the root mean square error (RMSE) by 14.6% in comparison to the
state-of-the-art models. In the scenario with sparse discrete observations, the
RMSE is diminished by 13.2%. Furthermore, the predictive results of the
proposed model show a more lucid representation of electromagnetic spatial
field distribution. To verify the universality of this model in radio map
construction tasks, the scenario of unknown radio emission source is
investigated. The results indicate that the proposed model is robust radio map
construction and accurate in predicting the location of the emission source.
