Generalisable convolutional neural network model for radio wave propagation in tunnels
出版年份 2023 全文链接
标题
Generalisable convolutional neural network model for radio wave propagation in tunnels
作者
关键词
-
出版物
IET Microwaves Antennas & Propagation
Volume -, Issue -, Pages -
出版商
Institution of Engineering and Technology (IET)
发表日期
2023-10-12
DOI
10.1049/mia2.12412
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- A Generalizable Indoor Propagation Model Based on Graph Neural Networks
- (2023) Sen Liu et al. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
- Towards Physics-Based Generalizable Convolutional Neural Network Models for Indoor Propagation
- (2022) Aristeidis Seretis et al. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
- Artificial Intelligence Enabled Radio Propagation for Communications—Part I: Channel Characterization and Antenna-Channel Optimization
- (2022) Chen Huang et al. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
- Artificial Intelligence Enabled Radio Propagation for Communications—Part II: Scenario Identification and Channel Modeling
- (2022) Chen Huang et al. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
- An Overview of Machine Learning Techniques for Radiowave Propagation Modeling
- (2021) Aristeidis Seretis et al. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
- Statistical Modeling of Electromagnetic Wave Propagation in Tunnels With Rough Walls Using the Vector Parabolic Equation Method
- (2019) Xingqi Zhang et al. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
- Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
- (2019) Yan Zhang et al. Applied Sciences-Basel
- Neural network-based path loss model for cellular mobile networks at 800 and 1800 MHz bands
- (2018) Sreevardhan Cheerla et al. AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS
- Physics-Based Optimization of Access Point Placement for Train Communication Systems
- (2018) Xingqi Zhang et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- A UHF Path Loss Model Using Learning Machine for Heterogeneous Networks
- (2017) M. Ayadi et al. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
- A Gaussian Beam Approximation Approach for Embedding Antennas Into Vector Parabolic Equation-Based Wireless Channel Propagation Models
- (2017) Xingqi Zhang et al. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
- Improvement of Outdoor Signal Strength Prediction in UHF Band by Artificial Neural Network
- (2016) Gilbert P. Ferreira et al. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
- A Hybrid Ray-Tracing/Vector Parabolic Equation Method for Propagation Modeling in Train Communication Channels
- (2016) Xingqi Zhang et al. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
- Error Analysis and Comparative Study of Numerical Methods for the Parabolic Equation Applied to Tunnel Propagation Modeling
- (2015) Xingqi Zhang et al. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
- A High-Accuracy ADI Scheme for the Vector Parabolic Equation Applied to the Modeling of Wave Propagation in Tunnels
- (2014) Xingqi Zhang et al. IEEE Antennas and Wireless Propagation Letters
- Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions
- (2012) Ignacio Fernández Anitzine et al. International Journal of Antennas and Propagation
- Modeling Radio Transmission Loss in Curved, Branched and Rough-Walled Tunnels With the ADI-PE Method
- (2010) Richard Martelly et al. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
- An ADI-PE Approach for Modeling Radio Transmission Loss in Tunnels
- (2009) Richard Martelly et al. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
- A Numerical Scheme for the Solution of the Vector Parabolic Equation Governing the Radio Wave Propagation in Straight and Curved Rectangular Tunnels
- (2009) P. Bernardi et al. IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
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