High-Speed Users' Mobility Prediction Scheme Based on Deep Learning for Small Cell and Femtocell Networks
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F22%3A10251111" target="_blank" >RIV/61989100:27740/22:10251111 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/61989100:27240/22:10251111
Výsledek na webu
<a href="https://link.springer.com/chapter/10.1007/978-3-030-92574-1_47" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-92574-1_47</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-92574-1_47" target="_blank" >10.1007/978-3-030-92574-1_47</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
High-Speed Users' Mobility Prediction Scheme Based on Deep Learning for Small Cell and Femtocell Networks
Popis výsledku v původním jazyce
Users' mobility has a huge impact on the performance of cellular networks. Acknowledge users' multiple next locations plays an important role in various aspects which can be mentioned as helping the base stations to pre-calculate and allocate the resource to users faster and more efficiently, shortening the duration of the handover process, reducing significantly the network data congestion, and increasing the overall users' satisfaction. In our article, we focus our attention on multiple users and multi-position ahead prediction for femtocells and small cells, typical of 5G infrastructure. We use Autoregressive Gated Recurrent Units (AR-GRU) to perform the prediction based on acknowledging users' trajectories. We use Simulation of Urban MObility (SUMO) to create our own users' trajectory datasets to train and test the models. In order to prove the effectiveness of the model, we compare its performance with Autoregressive Long Short-Term Memory (AR-LSTM), Deep Learning Neural Network (DNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models. Then we use the models in two more different datasets from two different simulated regions to prove the ability to work in different contexts. (C) 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Název v anglickém jazyce
High-Speed Users' Mobility Prediction Scheme Based on Deep Learning for Small Cell and Femtocell Networks
Popis výsledku anglicky
Users' mobility has a huge impact on the performance of cellular networks. Acknowledge users' multiple next locations plays an important role in various aspects which can be mentioned as helping the base stations to pre-calculate and allocate the resource to users faster and more efficiently, shortening the duration of the handover process, reducing significantly the network data congestion, and increasing the overall users' satisfaction. In our article, we focus our attention on multiple users and multi-position ahead prediction for femtocells and small cells, typical of 5G infrastructure. We use Autoregressive Gated Recurrent Units (AR-GRU) to perform the prediction based on acknowledging users' trajectories. We use Simulation of Urban MObility (SUMO) to create our own users' trajectory datasets to train and test the models. In order to prove the effectiveness of the model, we compare its performance with Autoregressive Long Short-Term Memory (AR-LSTM), Deep Learning Neural Network (DNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models. Then we use the models in two more different datasets from two different simulated regions to prove the ability to work in different contexts. (C) 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
20203 - Telecommunications
Návaznosti výsledku
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
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Lecture Notes in Networks and Systems. Volume 366
ISBN
978-3-030-92573-4
ISSN
2367-3370
e-ISSN
2367-3389
Počet stran výsledku
13
Strana od-do
446-458
Název nakladatele
Springer
Místo vydání
Cham
Místo konání akce
Thai Nguyen
Datum konání akce
1. 12. 2021
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—