High-Speed Users' Mobility Prediction Scheme Based on Deep Learning for Small Cell and Femtocell Networks
The result's identifiers
Result code in 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>
Alternative codes found
RIV/61989100:27240/22:10251111
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
High-Speed Users' Mobility Prediction Scheme Based on Deep Learning for Small Cell and Femtocell Networks
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
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
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Networks and Systems. Volume 366
ISBN
978-3-030-92573-4
ISSN
2367-3370
e-ISSN
2367-3389
Number of pages
13
Pages from-to
446-458
Publisher name
Springer
Place of publication
Cham
Event location
Thai Nguyen
Event date
Dec 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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