Intelligent super-fast Vehicle-to-Everything 5G communications with predictive switching between mmWave and THz links

Abstract

With the incoming of 5G communications, Vehicular Networks have the hope to achieve ultra-high data transmission rate with extremely low end-to-end delay. However, the dynamic nature of transportation traffic and increased data bandwidth demands are the major obstacles to achieve high transmission rate in Vehicular-to-Anything (V2X) Networks. To overcome these obstacles, this paper presents a novel Software Defined Networking (SDN)-controlled and Cognitive Radio (CR)-enabled V2X routing approach to achieve ultra-high data rate, by using predictive V2X routing that supports the intelligent switching between two 5G technologies: millimeter-wave (mmWave) and terahertz (THz). To improve the network management, Road Side units (RSUs) are used to segregate the V2X network into different clusters. Stability-aware clustering (SAC) scheme is also used for cluster formations. Our intelligent V2X is based on three features enabled machine learning approach: (1) To predict future 3D positions of the vehicles in the Cluster Heads (CHs) using Deep Neural Network with Extended Kalman Filter (DNN-EKF) algorithm for real-time, high-resolution prediction. (2) For THz communications, 0.3 THz to 3 THz band is selected for short-distance super-fast data transmissions. The THz band detection is performed by the CR-enabled Road Side Units (cRSUs). A Genetic Algorithm (GA)-based Improved Fruit Fly (GA-IFF) scheme is proposed to achieve an optimal route selection in THz communications. (3) In mmWave-based V2X communications, optimal beam selection is performed by the multi-type2 fuzzy inference system (M-T2FIS). By using these three intelligent designs approaches, we are able to achieve ultrahigh- rate and minimized transmission delay for short-range (in THz bands) and middle-range (in mmWave) communications. Finally, the proposed SDN-controlled, CR-enabled V2X Network is modeled and tested for performance evaluations with the metrics of delivery ratio, routing delay, protocol overhead, and data rate.