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.


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    Title :

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


    Contributors:
    Jung, Hongseok (author) / Jeong, Minsoo (author) / Hameed, Iqra (author) / Kim, Sunwoo (author)


    Publication date :

    2024-10-07


    Size :

    1251892 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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