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

机译：智能超快速车辆与MMWAVE和THZ链路之间的预测切换有5G通信

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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. (C) 2020 Elsevier Inc. All rights reserved.
机译：随着5G通信的传入，车辆网络的希望能够实现超高的数据传输速率，极低的端到端延迟。然而，运输流量的动态性质和增加的数据带宽需求是实现车辆到任何（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） - 控制和认知无线电（CR）为实现超高数据速率，通过支持两个5G之间的智能切换的预测V2X路由来实现超高数据速率技术：毫米波（MMWAVE）和太赫兹（THZ）。为了改进网络管理，路边单元（RSU）用于将V2X网络分成不同的集群。稳定感知群集（SAC）方案也用于群集形成。我们的智能V2X基于支持的机器学习方法：（1）使用具有扩展卡尔曼滤波器（DNN-EKF）算法的深神经网络预测集群头（CHS）中车辆中的未来3D位置，实时，高分辨率预测。 （2）对于THz通信，选择0.3至3至3个THz频段，用于短距离超快速数据传输。 THz频带检测由CR启用的Road侧单元（CRSUS）执行。提出了一种基于遗传算法（GA）改进的果蝇（GA-IFF）方案，以实现THZ通信中的最佳路径选择。 （3）在基于MMWAVE的V2X通信中，最佳波束选择由多型模糊推理系统（M-T2FIS）执行。通过使用这三种智能设计方法，我们能够实现超高速率并最小化用于短程（在THz带）和中范围（MMWAVE）通信中的传输延迟。最后，建议的SDN控制的CR启用的V2X网络建模和测试，用于具有传递比率，路由延迟，协议开销和数据速率的度量评估。 （c）2020 Elsevier Inc.保留所有权利。

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