An intelligent electromagnetic environment reconstruction method based on super-resolution generative adversarial network

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In this paper, an intelligent electromagnetic environment reconstruction method is proposed based on a super-resolution generative adversarial network (SRGAN). The altitude matrices together with the low-resolution matrices obtained by measured power values are employed as inputs. Then, an electromagnetic environment reconstruction method capable of generating the high-resolution power coverage matrix in the selected area is designed. Data enhancement is employed to expand the dataset and a modified generator network with squeeze and excitation modules is used during the training process. To validate the proposed method, the simulation analyses are carried out in typical suburb environments based on a ray-tracing tool. Numerical results indicate that, compared with classical methods such as nearest-neighbor interpolation, bilinear interpolation, and bicubic interpolation, the proposed method can provide more accurate reconstruction results for power coverage. In addition, the peak signal to noise ratio (PSNR) of the proposed method is higher than those of the classical methods. The proposed intelligent electromagnetic environment reconstruction method can be useful for the planning, deployment, and optimization of wireless networks.