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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27740/21:10249650

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

Users&apos; 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&apos; 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&apos; 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

Users&apos; 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&apos; 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&apos; 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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/LM2018140" target="_blank" >LM2018140: e-Infrastruktura CZ</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Bělehrad

Datum konání akce

23. 11. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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