Coordinated Machine Learning for Energy Efficient D2D Communication

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Coordinated Machine Learning for Energy Efficient D2D Communication

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Coordinated Machine Learning for Energy Efficient D2D Communication

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/GA23-05646S" target="_blank" >GA23-05646S: Inteligentní přidělovaní rádiových prostředků a řízení mobility založené na federovaném učení</a><br>

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

IEEE Wireless Communications Letters

ISSN

2162-2337

e-ISSN

2162-2345

Svazek periodika

13

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

5

Strana od-do

1493-1497

Kód UT WoS článku

001221294500063

EID výsledku v databázi Scopus

2-s2.0-85188001146