@inproceedings{a222757797864f8888d805392bd983ef,
title = "Safe Positioning Based on CNN and LSTM for 5G Wireless Networks",
abstract = "This paper presents a robust 5G wireless networks visual safety positioning model, which combines CNN (Convolutional Neural Network) and LSTM(Long Short-Term Memory) networks and can solve the sequence problem. Two parallel full connection layers are added to the output layer of the network to regress the RGB images to obtain the 3D position and 3D direction of the 5G wireless networks. Because the data set of each scene is small, the method of transfer learning is used in the training. The model has the best positioning result in the chess scene on the 7senses dataset, with the positioning error of 0.21m and 7.52°, and the positioning error in the seven scenes is 0.31m and 10.35°. This method can achieve good positioning effect in indoor positioning.",
keywords = "5G, CNN, LSTM, Positioning, Safety, Wireless networks",
author = "Lu Chen and Guan Mingxiang and Zhou Jianming and Wu Naixing and Gan Yuxi and Tang Hui",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; 2022 International Conference on Network Communication and Information Security, ICNCIS 2022 ; Conference date: 19-08-2022 Through 21-08-2022",
year = "2022",
doi = "10.1117/12.2657201",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Mohiuddin Ahmed",
booktitle = "International Conference on Network Communication and Information Security, ICNCIS 2022",
address = "United States",
}