Machine Learning for Channel Quality Prediction: From Concept to Experimental Validation
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Machine Learning for Channel Quality Prediction: From Concept to Experimental Validation
Original language description
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
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20203 - Telecommunications
Result continuities
Project
<a href="/en/project/LTT20004" target="_blank" >LTT20004: Cooperation with International Research Centre in Area of Digital Communication Systems</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
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Wireless Communications
ISSN
1536-1276
e-ISSN
1558-2248
Volume of the periodical
23
Issue of the periodical within the volume
10
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
14605-14619
UT code for WoS article
001338574900179
EID of the result in the Scopus database
2-s2.0-85197589715