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

Result code in 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>

Alternative codes found

RIV/61989100:27740/24:10255255

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20203 - Telecommunications

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Name of the periodical

Computer Networks

ISSN

1389-1286

e-ISSN

1872-7069

Volume of the periodical

252

Issue of the periodical within the volume

2024

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

16

Pages from-to

1-16

UT code for WoS article

001284294000001

EID of the result in the Scopus database

2-s2.0-85199770152