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.

随着5G通信的到来，车载网络希望以极低的端到端延迟实现超高数据传输速率。但是，交通运输的动态特性和对数据带宽的需求增加是在车对空（V2X）网络中实现高传输速率的主要障碍。为了克服这些障碍，本文提出了一种新颖的软件定义网络（SDN）控制和支持认知无线电（CR）的V2X路由方法，通过使用支持两个5G之间智能切换的预测性V2X路由来实现超高数据速率。技术：毫米波（mmWave）和太赫兹（THz）。为了改善网络管理，路边单元（RSU）用于将V2X网络隔离到不同的群集中。稳定性感知群集（SAC）方案也用于群集形成。我们的智能V2X基于支持机器学习的三种功能：（1）使用带有扩展卡尔曼滤波器（DNN-EKF）的深度神经网络实时预测簇头（CH）中车辆的未来3D位置，高分辨率预测。（2）对于THz通信，为短距离超快数据传输选择0.3 THz至3 THz频带。THz频段检测由启用CR的路边单元（cRSU）执行。提出了一种基于遗传算法的改进果蝇（GA-IFF）方案，以实现太赫兹通信中的最优路由选择。（3）在基于mmWave的V2X通信中，最佳光束选择是由multi-type2模糊推理系统（M-T2FIS）执行的。通过使用这三种智能设计方法，我们能够实现超高速率和最小化的短距离（在THz频段）和中距离（在毫米波）通信的传输延迟。最后，对拟议的SDN控制的，启用CR的V2X网络进行建模和测试，以评估性能，包括传递比率，路由延迟，协议开销和数据速率。