A High-Accuracy Deep Back-Projection CNN-Based Propagation Model for Tunnels
Hao Qin, Siyi Huang, Xingqi Zhang
Abstract
This letter proposes a high-accuracy deep back-projection convolutional neural network (DBPCNN)-based propagation model for radio wave prediction in long guiding structures such as tunnels. The model integrates convolutional neural networks (CNNs) with deterministic models to accelerate channel simulations by leveraging coarse-mesh received signal strength (RSS) data. An error compensation mechanism is introduced using the optimization-based iterative back-projection (IBP) algorithm, enhancing prediction accuracy and efficiency. The proposed model achieves accurate predictions of fine-mesh RSS with a large scale factor and demonstrates excellent generalization across various tunnel geometries. Extensive validation against numerical results and measurement campaigns in a real tunnel environment confirms the model's superior performance and potential practical utility.