@inproceedings{1f787a3e3d104cbab214d7447a9da0cd,
title = "Intelligent Wireless Propagation Model with Environmental Adaptability",
abstract = "Reasonable deployment of base stations can provide users with better services due to the development of 5G. During the deployment, wireless propagation models are used to predict signal coverage. Based on Deep Learning, this paper proposes an intelligent model, which can be adapted to a variety of environments and predict the user Reference Signal Receiving Power (RSRP). After introducing the data preparation, this paper presents the construction and training of the neural network, and then compares the intelligent model with the existing model. The results suggest that the intelligent model proposed can predict RSRP more accurately with stronger environmental adaptability. Therefore, in some cases, the model proposed can replace the existing model for deployment of base stations.",
keywords = "Deep learning, Neural network, Propagation model, RSRP",
author = "Xiaoyu Qu and Jiangyun Wang",
note = "Publisher Copyright: {\textcopyright} 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; Chinese Intelligent Systems Conference, CISC 2020 ; Conference date: 24-10-2020 Through 25-10-2020",
year = "2021",
doi = "10.1007/978-981-15-8450-3\_35",
language = "英语",
isbn = "9789811584497",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "326--332",
editor = "Yingmin Jia and Weicun Zhang and Yongling Fu",
booktitle = "Proceedings of 2020 Chinese Intelligent Systems Conference - Volume I",
address = "德国",
}