Machine Learning for Power Control in D2D Communication based on Cellular Channel Gains

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00336726" target="_blank" >RIV/68407700:21230/19:00336726 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning for Power Control in D2D Communication based on Cellular Channel Gains

Popis výsledku v původním jazyce

We consider a mobile network with users seeking to engage in a device-to-device (D2D) communication. Two D2D users (DUEs), a transmitter and a receiver, compose one D2D pair.We assume that the D2D pairs reuse a single communication channel to increase the spectral efficiency. Thus, a power control is needed to manage interference among the D2D pairs and to maximize capacity. We address the problem of D2D power control in the case when only standard cellular channel gains between the DUEs and base stations (BSs) are known while channel gains among DUEs are not available at all. We exploit supervised machine learning to determine transmission powers for individual D2D pairs. We show that the cellular channel gains can, in fact, be exploited to predict the transmission power setting for D2D pairs and, still, close-to-optimum sum capacity of the D2D pairs is reached. Moreover, even if our proposed power control requires no knowledge of the channel gains among DUEs and, thus, introduces no additional signalling, the sum capacity can be increased by 16% to 41:9% with respect to no power control, as demonstrated via simulations. Index Terms—Device-to-device; Power

Název v anglickém jazyce

Machine Learning for Power Control in D2D Communication based on Cellular Channel Gains

Popis výsledku anglicky

We consider a mobile network with users seeking to engage in a device-to-device (D2D) communication. Two D2D users (DUEs), a transmitter and a receiver, compose one D2D pair.We assume that the D2D pairs reuse a single communication channel to increase the spectral efficiency. Thus, a power control is needed to manage interference among the D2D pairs and to maximize capacity. We address the problem of D2D power control in the case when only standard cellular channel gains between the DUEs and base stations (BSs) are known while channel gains among DUEs are not available at all. We exploit supervised machine learning to determine transmission powers for individual D2D pairs. We show that the cellular channel gains can, in fact, be exploited to predict the transmission power setting for D2D pairs and, still, close-to-optimum sum capacity of the D2D pairs is reached. Moreover, even if our proposed power control requires no knowledge of the channel gains among DUEs and, thus, introduces no additional signalling, the sum capacity can be increased by 16% to 41:9% with respect to no power control, as demonstrated via simulations. Index Terms—Device-to-device; Power

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/GA17-17538S" target="_blank" >GA17-17538S: Kombinace radiofrekvenčního pásma a viditelného spektra pro přímou komunikaci mezi zařízeními</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

2019 IEEE Global Communications Conference (GLOBECOM) - Proceedings

ISBN

978-1-7281-0962-6

ISSN

2576-6813

e-ISSN

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Počet stran výsledku

6

Strana od-do

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Název nakladatele

American Institute of Physics and Magnetic Society of the IEEE

Místo vydání

San Francisco

Místo konání akce

Waikoloa, HI

Datum konání akce

9. 12. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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