Next-cell and mobility prediction in new generation cellular systems based on convolutional neural networks and encoding mobility data as images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10255255" target="_blank" >RIV/61989100:27240/24:10255255 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/24:10255255

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1389128624004894?pes=vor" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1389128624004894?pes=vor</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.comnet.2024.110657" target="_blank" >10.1016/j.comnet.2024.110657</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Next-cell and mobility prediction in new generation cellular systems based on convolutional neural networks and encoding mobility data as images

Popis výsledku v původním jazyce

Mobility prediction has been a popular research topic for many decades. With the advent of new generation technologies (5G and beyond) and smaller coverage cells, hand-over operations have become more frequent. Cellular system companies are therefore taking increasing interest in using the available predictive information on node movements to optimize and manage their bandwidth resources. In particular, the main challenging scope of our contribution consists in solving the issue of reliable next-cell prediction, aimed to call dropping probability minimization. In addition, our proposal is based on the innovative concept of mobility data to image encoding. The scheme is able to a-priori determine the next visited cells during host movements by applying a convolutional neural approach to mobility images. The power of machine learning is used to advantage, and highly accurate image classification is achieved for mobility prediction. We performed numerous simulation campaigns related to next-cell prediction in mobile cellular environments, obtaining very satisfactory results by the application of convolutional neural networks, which have an impressive history of effectiveness with image classification problems. The trained network has been associated to each coverage cell and the prediction accuracy has been evaluated.

Název v anglickém jazyce

Next-cell and mobility prediction in new generation cellular systems based on convolutional neural networks and encoding mobility data as images

Popis výsledku anglicky

Mobility prediction has been a popular research topic for many decades. With the advent of new generation technologies (5G and beyond) and smaller coverage cells, hand-over operations have become more frequent. Cellular system companies are therefore taking increasing interest in using the available predictive information on node movements to optimize and manage their bandwidth resources. In particular, the main challenging scope of our contribution consists in solving the issue of reliable next-cell prediction, aimed to call dropping probability minimization. In addition, our proposal is based on the innovative concept of mobility data to image encoding. The scheme is able to a-priori determine the next visited cells during host movements by applying a convolutional neural approach to mobility images. The power of machine learning is used to advantage, and highly accurate image classification is achieved for mobility prediction. We performed numerous simulation campaigns related to next-cell prediction in mobile cellular environments, obtaining very satisfactory results by the application of convolutional neural networks, which have an impressive history of effectiveness with image classification problems. The trained network has been associated to each coverage cell and the prediction accuracy has been evaluated.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Computer Networks

ISSN

1389-1286

e-ISSN

1872-7069

Svazek periodika

252

Číslo periodika v rámci svazku

2024

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

001284294000001

EID výsledku v databázi Scopus

2-s2.0-85199770152