A deep learning-based model for High-Speed Users' Mobility Prediction in Small Cell and Femtocell Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10249650" target="_blank" >RIV/61989100:27240/21:10249650 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/21:10249650
Result on the web
<a href="https://ieeexplore.ieee.org/document/9653254" target="_blank" >https://ieeexplore.ieee.org/document/9653254</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TELFOR52709.2021.9653254" target="_blank" >10.1109/TELFOR52709.2021.9653254</a>

Result language
angličtina
Original language name
A deep learning-based model for High-Speed Users' Mobility Prediction in Small Cell and Femtocell Networks
Original language description
Users' mobility has a huge impact on the performance of cellular networks. Particularly in the networks which are deployed with small cells, by predicting the next positions of the users, it can determine the nearby cells to the users before they arrive and prepare the connection, and estimate the mobile resources for them. In this paper, we proposed a model to predict the users' next location based on Recurrent Neural Network (RNN) with Long-Short Term Memory (LSTM) cell, a Deep learning neural network. We use Simulation of Urban MObility (SUMO) to create our own users' trajectory datasets to train and test the models. To prove the effectiveness of the model, we compare its performance with Deep Neural Network (DNN), and Gated Recurrent Unit (GRU) models, Baseline model (BL), and Linear regression model (LR). (C) 2021 IEEE.
Czech name
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Czech description
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Project
<a href="/en/project/LM2018140" target="_blank" >LM2018140: e-Infrastructure CZ</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2021 29th Telecommunications Forum, TELFOR : Proceedings of Papers = XXIX Telekomunikacioni Forum, TELFOR 2021 : Zbornik radova : Online event : Belgrade, Serbia, November, 23-24, 2021
ISBN
978-1-66542-584-1
ISSN
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e-ISSN
—
Number of pages
4
Pages from-to
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Publisher name
IEEE
Place of publication
Piscataway
Event location
Bělehrad
Event date
Nov 23, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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