Machine Learning-Based Beamforming for Unmanned Aerial Vehicles Equipped with Reconfigurable Intelligent Surfaces

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00363402" target="_blank" >RIV/68407700:21230/22:00363402 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/MWC.004.2100694" target="_blank" >https://doi.org/10.1109/MWC.004.2100694</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/MWC.004.2100694" target="_blank" >10.1109/MWC.004.2100694</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning-Based Beamforming for Unmanned Aerial Vehicles Equipped with Reconfigurable Intelligent Surfaces

Popis výsledku v původním jazyce

Unmanned aerial vehicles (UAVs) equipped with reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for numerous applications involving aerial networks. However, the UAV-RIS concept faces challenges related to the deployment of the UAV-RIS, especially in cases, where UAV-RIS is combined with emerging technologies, such as beamforming, sensitive to propagation channel variation. In this article, we first overview various use-cases of UAV-RIS beam-forming considering practical scenarios. Aiming to improve the performance of communication channels, we propose a machine learning-based beamforming policy for UAV-RIS by employing prioritized experience replay (PER) based deep Q-Network (DQN). Compared to traditional approaches, the proposed PER DQN-based beamforming for UAV-RIS communication provides significant enhancements in performance. Finally, we highlight some potential directions for future research.

Název v anglickém jazyce

Machine Learning-Based Beamforming for Unmanned Aerial Vehicles Equipped with Reconfigurable Intelligent Surfaces

Popis výsledku anglicky

Unmanned aerial vehicles (UAVs) equipped with reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for numerous applications involving aerial networks. However, the UAV-RIS concept faces challenges related to the deployment of the UAV-RIS, especially in cases, where UAV-RIS is combined with emerging technologies, such as beamforming, sensitive to propagation channel variation. In this article, we first overview various use-cases of UAV-RIS beam-forming considering practical scenarios. Aiming to improve the performance of communication channels, we propose a machine learning-based beamforming policy for UAV-RIS by employing prioritized experience replay (PER) based deep Q-Network (DQN). Compared to traditional approaches, the proposed PER DQN-based beamforming for UAV-RIS communication provides significant enhancements in performance. Finally, we highlight some potential directions for future research.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/LTT20004" target="_blank" >LTT20004: Spolupráce s mezinárodním výzkumným centrem v oblasti digitálních komunikačních systémů</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

IEEE WIRELESS COMMUNICATIONS

ISSN

1536-1284

e-ISSN

1558-0687

Svazek periodika

29

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

7

Strana od-do

32-38

Kód UT WoS článku

000870727800015

EID výsledku v databázi Scopus

2-s2.0-85140888583