Integrating UAVs as Transparent Relays into Mobile Networks: A Deep Learning Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00345389" target="_blank" >RIV/68407700:21230/20:00345389 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/PIMRC48278.2020.9217280" target="_blank" >http://dx.doi.org/10.1109/PIMRC48278.2020.9217280</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/PIMRC48278.2020.9217280" target="_blank" >10.1109/PIMRC48278.2020.9217280</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Integrating UAVs as Transparent Relays into Mobile Networks: A Deep Learning Approach

Popis výsledku v původním jazyce

Since flying base stations (FlyBSs) are energy constrained, it is convenient for them to act as transparent relays with minimal communication control and management functionalities. The challenge when using the transparent relays is the inability to measure the relaying channel quality between the relay and user equipment (UE). This channel quality information is required for communication-related functions, such as the UE association, however, this information is not available to the network. In this letter, we show that it is possible to determine the UEs' association based only on the information commonly available to the network, i.e., the quality of the cellular channels between conventional static base stations (SBSs) and the UEs. Our proposed association scheme is implemented through deep neural networks, which capitalize on the mutual relation between the unknown relaying channel from any UE to the FlyBS and the known cellular channels from this UE to multiple surrounding SBSs. We demonstrate that our proposed framework yields a sum capacity that is close to the capacity reached by solving the association via exhaustive search.

Název v anglickém jazyce

Integrating UAVs as Transparent Relays into Mobile Networks: A Deep Learning Approach

Popis výsledku anglicky

Since flying base stations (FlyBSs) are energy constrained, it is convenient for them to act as transparent relays with minimal communication control and management functionalities. The challenge when using the transparent relays is the inability to measure the relaying channel quality between the relay and user equipment (UE). This channel quality information is required for communication-related functions, such as the UE association, however, this information is not available to the network. In this letter, we show that it is possible to determine the UEs' association based only on the information commonly available to the network, i.e., the quality of the cellular channels between conventional static base stations (SBSs) and the UEs. Our proposed association scheme is implemented through deep neural networks, which capitalize on the mutual relation between the unknown relaying channel from any UE to the FlyBS and the known cellular channels from this UE to multiple surrounding SBSs. We demonstrate that our proposed framework yields a sum capacity that is close to the capacity reached by solving the association via exhaustive search.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/GA18-27023S" target="_blank" >GA18-27023S: Komunikace v samo-optimalizujících se mobilních sítích s drony</a><br>

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications

ISBN

978-1-7281-4490-0

ISSN

1558-2612

e-ISSN

—

Počet stran výsledku

6

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Boston

Místo konání akce

London

Datum konání akce

31. 8. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000631491700191