Machine Learning-Based Channel Quality Prediction in 6G Mobile Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00369232" target="_blank" >RIV/68407700:21230/23:00369232 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/MCOM.001.2200305" target="_blank" >https://doi.org/10.1109/MCOM.001.2200305</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/MCOM.001.2200305" target="_blank" >10.1109/MCOM.001.2200305</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning-Based Channel Quality Prediction in 6G Mobile Networks

Popis výsledku v původním jazyce

Channel quality is an essential information for management of radio resources in mobile networks. To acquire the channel quality information, pilot (or reference) signals are commonly transmitted, measured, and reported to the network. However, the process of channel quality acquisition is both time and energy consuming. Moreover, the radio resources are competitively shared by the pilot signals and users' data. This motivates an employment of prediction-based approaches determining the channel quality at low cost to avoid over-consumption of resources for pilots. Machine learning is seen as an efficient way to deal with the channel quality prediction, since it allows to reveal usually hidden relations among known and unknown channel quality measurements. In this article, we first overview state-of-the-art works leveraging the time, frequency, and spatial correlations among already known channel qualities and the channel(s), whose quality should be predicted. Furthermore, we outline a framework for a network correlation-based channel prediction enabling to determine the quality of unknown channel between any two communicating nodes by knowing only channels of these two nodes to reference nodes. Then, we demonstrate use-cases and application scenarios for all machine learning-based channel quality predictions. We also assess potential reduction in channel quality measurement-related overhead by all approaches to demonstrate their complementarity and capabilities to support low-overhead and energy-friendly massive deployment of devices in 6G mobile networks.

Název v anglickém jazyce

Machine Learning-Based Channel Quality Prediction in 6G Mobile Networks

Popis výsledku anglicky

Channel quality is an essential information for management of radio resources in mobile networks. To acquire the channel quality information, pilot (or reference) signals are commonly transmitted, measured, and reported to the network. However, the process of channel quality acquisition is both time and energy consuming. Moreover, the radio resources are competitively shared by the pilot signals and users' data. This motivates an employment of prediction-based approaches determining the channel quality at low cost to avoid over-consumption of resources for pilots. Machine learning is seen as an efficient way to deal with the channel quality prediction, since it allows to reveal usually hidden relations among known and unknown channel quality measurements. In this article, we first overview state-of-the-art works leveraging the time, frequency, and spatial correlations among already known channel qualities and the channel(s), whose quality should be predicted. Furthermore, we outline a framework for a network correlation-based channel prediction enabling to determine the quality of unknown channel between any two communicating nodes by knowing only channels of these two nodes to reference nodes. Then, we demonstrate use-cases and application scenarios for all machine learning-based channel quality predictions. We also assess potential reduction in channel quality measurement-related overhead by all approaches to demonstrate their complementarity and capabilities to support low-overhead and energy-friendly massive deployment of devices in 6G mobile networks.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/LTT20004" target="_blank" >LTT20004: Spolupráce s mezinárodním výzkumným centrem v oblasti digitálních komunikačních systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

IEEE Communications Magazine

ISSN

0163-6804

e-ISSN

1558-1896

Svazek periodika

61

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

7

Strana od-do

106-112

Kód UT WoS článku

001055083700019

EID výsledku v databázi Scopus

2-s2.0-85166732755