In the near-field region of an extremely large-scale MIMO (XL MIMO) system, channel reconstruction can be solved by utilizing sparse parameter estimation after transforming the received pilots at the base station (BS) into Cartesian domain. However, the process of exhaustive search over the codebook consumes significant computational resources and running time, especially when dealing with a vast number of antennas. In this study, we visualize the sparse channel matrix in the Cartesian domain as an channel image and propose a deep neural network, i.e., channel keypoint detection network (CKNet), to locate the user and scatterers. Subsequently, we employ a straightforward Newton optimization module to fine-tune the estimations. Experimental results demonstrate that the CKNet-empowered channel reconstruction scheme substantially reduces computational complexity while maintaining high accuracy in both user and scatterer localization and channel reconstruction.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Efficient Near-Field User Localization and Channel Reconstruction via Image Keypoint Detection


    Beteiligte:
    Li, Mengyuan (Autor:in) / Han, Yu (Autor:in) / Jin, Shi (Autor:in)


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    2124604 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Keypoint based action localization

    KADAV ASIM / LAI FARLEY / GRAF HANS PETER et al. | Europäisches Patentamt | 2025

    Freier Zugriff

    KEYPOINT BASED ACTION LOCALIZATION

    KADAV ASIM / LAI FARLEY / GRAF HANS PETER et al. | Europäisches Patentamt | 2022

    Freier Zugriff

    Progressive Keypoint Detection With Dense Siamese Network for SAR Image Registration

    Xiang, Deliang / Xu, Yihao / Cheng, Jianda et al. | IEEE | 2023


    MONOCULAR 2D SEMANTIC KEYPOINT DETECTION AND TRACKING

    CHEN HAOFENG / ARJUN BHARGAVA / RARES ANDREI AMBRUS et al. | Europäisches Patentamt | 2023

    Freier Zugriff