In cellular vehicular communications, high density and mobility of vehicles require frequent resource allocation, which can cause network congestion and large signalling and processing delay. To overcome this problem, we propose a novel semi-persistent resource allocation scheme based on a two-tier heterogeneous network architecture. The architecture includes a central macro base station (MBS) and multiple roadside units (RSU). In the proposed semi-persistent scheme, the MBS pre-allocates persistent resource to RSUs based on predicted traffic, and then allocates dynamic resource upon real-time requests from RSUs while vehicles simultaneously communicate using the pre-allocated resource. A simple Space-Time k-Nearest Neighbour (ST-kNN) method is developed for short-term traffic prediction, and a geometric water-filling algorithm is developed for minimizing the relative latency. Simulation results validate the effectiveness of the proposed semi-persistent scheme in comparison with two benchmark schemes.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Semi-Persistent Resource Allocation Based on Traffic Prediction for Vehicular Communications


    Contributors:

    Published in:

    Publication date :

    2020-06-01


    Size :

    1651832 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Semi-Persistent V2X Resource Allocation with Traffic Prediction in Two-Tier Cellular Networks

    Chu, Ping / Zhang, J. Andrew / Wang, Xiaoxiang et al. | IEEE | 2019


    CONTEXT BASED VEHICULAR TRAFFIC PREDICTION

    WANG ZHI HU / ZHAO SHIWAN / LIU JING LAN et al. | European Patent Office | 2020

    Free access

    Context based vehicular traffic prediction

    WANG ZHI HU / ZHAO SHIWAN / LIU JING LAN et al. | European Patent Office | 2023

    Free access

    Energy Efficient Resource Allocation in Vehicular Cloud based Architecture

    Alahmadi, Amal A. / Musa, Mohammed O. I. / El-Gorashi, T. E. H. et al. | ArXiv | 2019

    Free access

    Learning-Based Resource Allocation for Backscatter-Aided Vehicular Networks

    Khan, Wali Ullah / Nguyen, Tu N. / Jameel, Furqan et al. | IEEE | 2022