In this paper, we propose a graph convolutional network (GCN)-based cooperative localization method for 5G mobile networks using round-trip time (RTT) and angle of arrival (AoA) measurements. Implemented in an Urban Macrocell (UMa) scenario with non-line-of-sight (NLOS) conditions and high measurement noise, our approach enhances localization accuracy and reduces computational complexity. Compared with existing cooperative and non-cooperative methods, our simulation results demonstrate that the proposed method satisfies the 3GPP Release 17 requirements, achieving a horizontal accuracy of less than 0.94 m for 90% of user equipments (UEs) and a computational time of 0.60 ms even in non-line of sight (NLOS) environments. This method leverages sidelink-positioning reference signals (SLPRS) to facilitate device-to-device positioning without base station communication, proving effective in challenging environments.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    GCN-based Cooperative Localization using Sidelink Communication in 3GPP Urban Environment


    Beteiligte:
    Jung, Hongseok (Autor:in) / Jeong, Minsoo (Autor:in) / Hameed, Iqra (Autor:in) / Kim, Sunwoo (Autor:in)


    Erscheinungsdatum :

    07.10.2024


    Format / Umfang :

    1251892 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    RPAS communication channels based on WCDMA 3GPP standard

    Andrii Grekhov / Vasyl Kondratiuk / Svitlana Ilnytska | DOAJ | 2020

    Freier Zugriff

    Distributed Antenna System in 3GPP Specified Industrial Environment

    Sheikh, Muhammad Usman / Ruttik, Kalle / Jantti, Riku et al. | IEEE | 2021


    Throughput Performance of 3GPP LTE System in Railway Environment

    Brunel, Loïc / Bonneville, Hervé / Charaf, Akl | British Library Conference Proceedings | 2017