Environmental Knowledge-Driven Over-the-Horizon Propagation Loss Prediction Based on Short- and Long-Term Parallel Double-Flow TrellisNets-Reference-Cited by-同舟云学术

Environmental Knowledge-Driven Over-the-Horizon Propagation Loss Prediction Based on Short- and Long-Term Parallel Double-Flow TrellisNets

Published:2023-12 Issue:12 Volume:71 Page:9799-9813
ISSN:0018-926X
Container-title:IEEE Transactions on Antennas and Propagation
language:
Short-container-title:IEEE Trans. Antennas Propagat.

Author:

Wu Jiajing¹^ORCID,Wei Zhiqiang²^ORCID,Zhang Jinpeng¹^ORCID,Guo Xiangming¹^ORCID,Jia Dongning²,Xu Jiali³^ORCID,Zhang Yushi¹,Li Qingliang¹,Zhou Huiyu⁴^ORCID

Affiliation:

1. National Key Laboratory of Electromagnetic Environment, China Research Institute of Radiowave Propagation, Qingdao, China

2. School of Information Science and Engineering, Ocean University of China, Qingdao, China

3. Pilot National Laboratory for Marine Science and Technology, Qingdao, China

4. School of Computing and Mathematical Sciences, University of Leicester, Leicester, U.K.

Funder

Key Research and Development Projects of Shandong Province

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/8/10367424/10122850.pdf?arnumber=10122850

Reference55 articles.

1. Denoising of Over-the-Horizon Propagation Loss Based on 1D Convolutional Autoencoder

2. An improved impedance-boundary algorithm for Fourier split-step solutions of the parabolic wave equation

3. Dynamic Heterogeneous Learning Games for Opportunistic Access in LTE-Based Macro/Femtocell Deployments

4. A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series

5. Deep learning and its applications to machine health monitoring