A4 Artikkeli konferenssijulkaisussa
Multi-agent Deep Reinforcement Learning-based Trajectory Design for UAV-aided Edge Computing System (2023)

Lu, G., & Chang, Z. (2023). Multi-agent Deep Reinforcement Learning-based Trajectory Design for UAV-aided Edge Computing System. In WCNC 2023 : Proceedings of the IEEE Wireless Communications and Networking Conference. IEEE. IEEE Wireless Communications and Networking Conference. https://doi.org/10.1109/wcnc55385.2023.10118895

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Lu, Gengyuan; Chang, Zheng

Emojulkaisu: WCNC 2023 : Proceedings of the IEEE Wireless Communications and Networking Conference

Konferenssin paikka ja aika: Glasgow, United Kingdom, 26.-29.3.2023

ISBN: 978-1-6654-9123-5

eISBN: 978-1-6654-9122-8

Lehti tai sarja: IEEE Wireless Communications and Networking Conference

ISSN: 1525-3511

eISSN: 1558-2612

Julkaisuvuosi: 2023

Kustantaja: IEEE

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.1109/wcnc55385.2023.10118895

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Tiivistelmä

The unmanned aerial vehicle (UAV)-aided communication system is able to extend the coverage of the cellular network and provide flexible connectivity to the ground users. Recently, in order to achieve the ambitious goal of ubiquitous computing and intelligence, UAV-aided mobile edge computing (MEC) system also attracts significant research interests. In this work, we investigate the trajectory design for a multi-UAV-aided MEC system to maximize the defined quality of experience (QoE) and service fairness of multiple ground user equipments (UEs). Since the practical communication environment is complex and time-varying, it is challenging for the multiple UAVs to dynamically make autonomous decisions. Meanwhile, the centralized decision-making scheme may also have a certain degree of transmission delay and affect efficiency of the considered system. Therefore, bearing in mind these challenges, in order to solve the formulated problem, we propose a multi-agent deep reinforcement learning algorithm using Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to design UAV’s trajectory via optimizing the flying angle and distance. The simulation results show that our proposed solution outperforms the traditional algorithms.

YSO-asiasanat: miehittämättömät ilma-alukset; tieto- ja viestintätekniikka; viestintätekniikka; tietojärjestelmät

Vapaat asiasanat: mobile edge computing; unmanned aerial vehicle; multi-agent deep reinforcement learning; trajectory design

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2023

Alustava JUFO-taso: 1