Applying of network slicing in vehicular networks becomes a promising paradigm to support emerging Vehicle-to-Vehicle (V2V) applications with diverse quality of service (QoS) requirements. However, achieving effective network slicing in dynamic vehicular communications still faces many challenges, particularly time-varying traffic of Vehicle-to-Vehicle (V2V) services and the fast-changing network topology. By leveraging the widely deployed LTE infrastructures, we propose a semi-decentralized network slicing framework in this paper based on the C-V2X Mode-4 standard to provide customized network slices for diverse V2V services. With only the long-term and partial information of vehicular networks, eNodeB (eNB) can infer the underlying network situation and then intelligently adjust the configuration for each slice to ensure the long-term QoS performance. Under the coordination of eNB, each vehicle can autonomously select radio resources for its V2V transmission in a decentralized manner. Specifically, the slicing control at the eNB is realized by a model-free deep reinforcement learning (DRL) algorithm, which is a convergence of Long Short Term Memory (LSTM) and actor-critic DRL. Compared to the existing DRL algorithms, the proposed DRL neither requires any prior knowledge nor assumes any statistical model of vehicular networks. Furthermore, simulation results show the effectiveness of our proposed intelligent network slicing scheme.
翻译:将网络切片应用于车辆网络,这已成为一种有希望的范例,可据以支持正在形成的具有不同服务质量要求的车辆到车辆(V2V)应用程序。然而,在动态车辆通信中实现有效的网络切片服务仍面临许多挑战,特别是车辆到车辆(V2V)服务和快速变化的网络地形图层在时间上的变化,车辆到车辆到车辆(V2V)服务的交通和快速变化的网络图层。通过利用广泛部署的LTE基础设施,我们提议在本文件中建立一个半分散化的网络切片框架,以C-V2X模式-4标准为基础,为不同的V2V2V服务提供定制的网络切片。只有车辆网络的长期和部分信息, eNodeB(eNB)才能推断基本的网络状况,然后明智地调整每个片段的配置,以确保长期的QOS。在电子定位的协调下,每个车辆可以以分散的方式自主地选择用于传输V2VV的无线电资源模式。具体地说,电子数据库的切片控制是通过不设模型的深层SL(L)网络的升级和现有的SDR(SL)的升级,这要求任何长期的升级的升级的升级的升级的模型和升级的升级的升级的升级的系统。