5G-based millimeter Waves (mmWave) systems have the prospective of enabling $>1Gbps$ communications in the Intelligent Transportation Systems (ITS). ITS relies on vehicle-to-everything (V2X) communications to share information among vehicles. However, the V2X Communications via existing technologies such as DSRC, 3G, 4G and LTE, are not able to achieve such a high data rate. Although 5G-based mmWave can support ultra-low-delay V2X transmissions, it comes with beam alignment difficulties as well as the routing stability issues due to rapid mobility of vehicles. The dynamic vehicle traffic causes frequent beam misalignment which tends to degrade the quality-of-service (QoS) performance. In this paper, we first propose a 3D-based position detection scheme for beam alignment/selection purpose. Then a group-based routing algorithm is performed to select a secure path for achieving trustworthy data transmissions. The road traffic is automatically segmented to divide the vehicles into different groups, and each group head is selected and members are added. Group members are authenticated by the group head via elliptic curve algorithms. Huffman coding is performed to compress the data and encrypt the binary files. This proposed novel intelligent beam control and secure stable routing scheme have been verified in simulations to demonstrate much better performance than existing schemes.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Intelligent Vehicle Network Routing With Adaptive 3D Beam Alignment for mmWave 5G-Based V2X Communications


    Contributors:


    Publication date :

    2021-05-01


    Size :

    7698491 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Radio Maps for Beam Alignment in mmWave Communications with Location Uncertainty

    Ha, Tien Ngoc / Romero, Daniel / Lopez-Valcarce, Roberto | IEEE | 2024


    MmWave Vehicular Beam Alignment Leveraging Online Learning

    Xian, Qingyang / Doufexi, Angela / Armour, Simon | IEEE | 2023


    On Spectrum Sensing for mmWave and THz Beam-based Communications

    Zang, Junwei / Liu, Qiao / He, Jia et al. | IEEE | 2023


    BsNet: A Deep Learning-Based Beam Selection Method for mmWave Communications

    Lin, Chia-Hung / Kao, Wei-Cheng / Zhan, Shi-Qing et al. | IEEE | 2019