Machine Learning for Channel Quality Prediction: From Concept to Experimental Validation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00377828" target="_blank" >RIV/68407700:21230/24:00377828 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/TWC.2024.3417532" target="_blank" >https://doi.org/10.1109/TWC.2024.3417532</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TWC.2024.3417532" target="_blank" >10.1109/TWC.2024.3417532</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning for Channel Quality Prediction: From Concept to Experimental Validation

Popis výsledku v původním jazyce

We focus on prediction of channel quality between any two devices using Deep Neural Network (DNN) from information already known to mobile networks. The DNN-based prediction reduces a cost of a common pilot-based channel quality measurement in scenarios with many ad-hoc communicating devices. However, collecting a sufficient number of high-quality and well-distributed training samples in real-world is not feasible. Hence, in this paper, we develop and validate a concept of DNN-based channel quality prediction between any two devices based on a low-complexity and easy-to-create digital twin. The digital twin serves for a generation of a large synthetic training dataset for channel quality prediction. As the low-complexity digital twin cannot capture all real-world aspects of the channels, we enhance the digital twin with real-world measured and artificially augmented inputs via transfer learning. The proposed concept is implemented and validated in software defined mobile network. We demonstrate that the propo

Název v anglickém jazyce

Machine Learning for Channel Quality Prediction: From Concept to Experimental Validation

Popis výsledku anglicky

We focus on prediction of channel quality between any two devices using Deep Neural Network (DNN) from information already known to mobile networks. The DNN-based prediction reduces a cost of a common pilot-based channel quality measurement in scenarios with many ad-hoc communicating devices. However, collecting a sufficient number of high-quality and well-distributed training samples in real-world is not feasible. Hence, in this paper, we develop and validate a concept of DNN-based channel quality prediction between any two devices based on a low-complexity and easy-to-create digital twin. The digital twin serves for a generation of a large synthetic training dataset for channel quality prediction. As the low-complexity digital twin cannot capture all real-world aspects of the channels, we enhance the digital twin with real-world measured and artificially augmented inputs via transfer learning. The proposed concept is implemented and validated in software defined mobile network. We demonstrate that the propo

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)<br>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

IEEE Transactions on Wireless Communications

ISSN

1536-1276

e-ISSN

1558-2248

Svazek periodika

23

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

14605-14619

Kód UT WoS článku

001338574900179

EID výsledku v databázi Scopus

2-s2.0-85197589715