Coordinated Machine Learning for Energy Efficient D2D Communication

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00375203" target="_blank" >RIV/68407700:21230/24:00375203 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/LWC.2024.3377444" target="_blank" >https://doi.org/10.1109/LWC.2024.3377444</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/LWC.2024.3377444" target="_blank" >10.1109/LWC.2024.3377444</a>

Result language
angličtina
Original language name
Coordinated Machine Learning for Energy Efficient D2D Communication
Original language description
We address the problem of a coordination among machine learning tools solving different problems of radio resource management. We focus on energy efficient device-to-device (D2D) communication in a scenario with many devices communicating adhoc directly with each other. In such scenario, deep neural network (DNN) is a convenient tool to predict the channel quality among devices and to control the transmission power. However, addressing both problems by a single DNN is not suitable due to a dependency of the power control on the predicted channel quality. Similarly, a simple concatenation of two DNNs leads to a high cumulative learning error and an inevitable performance degradation. Hence, we propose a mutual coordination of the DNNs for channel quality prediction and for power control via a feedback and a knowledge transfer to mitigate the accumulation of errors in individual learned models. The proposed coordination improves the energy efficiency by 10-69% compared to state-of-the-art works and reduces the training time of DNNs more than 3.5-times compared to DNNs without coordination.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20203 - Telecommunications

Project
<a href="/en/project/GA23-05646S" target="_blank" >GA23-05646S: Intelligent Radio Resource and Mobility Management based on Federated Learning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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