MultiPath Component (MPC) tracking aims to trace the path parameter series in time-varying channel, which is vital in wireless mobile network. Conventional MPC tracking solution relies on the heuristic Multipath Component Distance (MCD), which cannot directly characterize the user mobility process. In this paper, Virtual User Position (VUP) is proposed to represent the geometric information of MPCs, which can reflect the user mobility in time-varying channel. Owing to the explicit location information in VUP, a deep learning based MPC Tracking neural Network (MPCTNet) is proposed inspired by similarities between MPC tracking and Multiple Object Tracking (MOT) in computer vision. To train MPCTNet more efficiently, a two-stage hierarchical training scheme is presented as well. Simulation results reveal the superiority of proposed MPCTNet over other conventional MCD based solutions.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Novel Deep Learning Based Time-Varying Multipath Component Tracking Algorithm


    Contributors:
    Wang, Haoyu (author) / Sun, Zhi (author)


    Publication date :

    2024-06-24


    Size :

    1275021 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Performance of Turbo Decoding for Time-Varying Multipath Channels

    Gose, E. / Buyukatak, K. / Osman, O. et al. | British Library Conference Proceedings | 2003


    Performance of turbo decoding for time-varying multipath channels

    Gose, E. / Buyukatak, K. / Osman, O. et al. | IEEE | 2003


    The issue of spreading in multipath time-varying channels

    Medard,M. / Gallager,R.G. / Massachusetts Inst.of Technol.,Lab.for Information and Decision Systems,US | Automotive engineering | 1995


    UAV Dynamic Tracking Algorithm Based on Deep learning

    Chen, Sifan / Yang, Haoran / Zhang, Ang et al. | IEEE | 2021