A method for elevated ducts refinement based on convolutional neural network

Abstract

Elevated duct (EleD) is an abnormal atmospheric refraction structure with a suspended trapped layer. The precise and highly resolved elevated duct-height-based data (EleDH) is crucial for radio communication systems, especially in electromagnetic wave path loss prediction and EleDH-producing systems. However, producing high-resolution EleDH is challenging because of the massive details in the EleDH data. Direct and high-time refinement procedures mostly lead to unrealistic outcomes. The study provides a Dense-Linear convolutional neural network (DLCNN)-based EleDH refinement technique based on the development of statistical downscaling and super-resolution technologies. Additionally, the stack approach is used, and the refining order is taken into consideration to ensure precision in high-time refinement and provide reliable outcomes. To demonstrate the strength of DLCNN in capturing complex internal characteristics of EleDH, a new EleD data set is first funded, which only contains the duct height. From this data set, we use the duct height as the core refinement of the EleD's trapped layer and the thickness of the trapped layer to ensure reliable duct height. Seven super-resolution models are utilized for fair comparisons. The experimental results prove that the DLCNN has the highest refinement performance; also, it obtained excellent generalization capacity, where the minimum and maximum obtained Accuracy(20%), MAE, and RMSE were 85.22% ∓ 88.30%, 36.09 ∓ 45.97 and 8.68 ∓ 10.14, respectively. High-resolution EleDH improves path loss prediction, where the minimum and maximum obtained bias were 2.37 ∓ 9.51 dB.