A High-Accuracy Deep Back-Projection CNN-Based Propagation Model for Tunnels

被引：2

作者：

Qin, Hao ^{[1
,2
]}

Huang, Siyi ^{[1
,2
]}

Zhang, Xingqi ^{[1
,2
]}

机构：

[1] Univ Coll Dublin, Sch Elect & Elect Engn, Dublin D04 V1W8, Ireland

[2] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB, Canada

来源：

IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS | 2024年 / 23卷 / 03期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Artificial intelligence; convolutional neural network (CNN); parabolic wave equation; tunnel propagation; WIRELESS PROPAGATION; COMMUNICATION;

D O I：

10.1109/LAWP.2023.3341882

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：1015 / 1019

页数：5