This paper presents a joint fine time synchronization and channel estimation scheme based on deep learning (DL) for wireless communication systems. The scheme adopts a specific training sequence structure with both cyclic prefixing and cyclic postfixing. It works excellently without setting a search range and a threshold as required by the conventional method based on the same training sequence structure. Simulation results demonstrate that the proposed DL-based scheme has significant performance gains for most cases as compared with the conventional method. With improved time synchronization, better channel estimation performance is achieved accordingly.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Joint Fine Time Synchronization and Channel Estimation Using Deep Learning for Wireless Communication Systems


    Beteiligte:


    Erscheinungsdatum :

    01.06.2022


    Format / Umfang :

    546617 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Channel Estimation for UAV Communication Systems Using Deep Neural Networks

    Ahmed Al-Gburi / Osamah Abdullah / Akram Y. Sarhan et al. | DOAJ | 2022

    Freier Zugriff


    Enhancing Least Square Channel Estimation Using Deep Learning

    Gizzini, Abdul Karim / Chafii, Marwa / Nimr, Ahmad et al. | IEEE | 2020



    Adaptive Channel Estimation based on Deep Learning

    Gizzini, Abdul Karim / Chafii, Marwa / Nimr, Ahmad et al. | IEEE | 2020