面向可靠V2V服务提供的半分散式网络切片：一种无模型深度强化学习方法
Semi-Decentralized Network Slicing for Reliable V2V Service Provisioning: A Model-free Deep Reinforcement Learning Approach
摘要：
在车辆网络中应用网络切片成为支持具有不同服务质量（QoS）要求的新兴车辆到车辆（V2 V）应用的有前途的范例。然而，在动态车辆通信中实现有效的网络切片仍然面临许多挑战，特别是车辆到车辆（V2 V）服务的时变流量和快速变化的网络拓扑。通过利用广泛部署的LTE基础设施，本文提出了一种基于C-V2X Mode-4标准的半分散式网络切片框架，为各种V2 V服务提供定制的网络切片。在仅获得车辆网络的长期和部分信息的情况下，eNodeB（eNB）可以推断底层网络状况，然后智能地调整每个切片的配置，以确保长期的QoS性能。在eNB的协调下，每个车辆可以以分散的方式自主地选择用于其V2 V传输的无线电资源。具体地，eNB处的切片控制通过无模型深度强化学习（DRL）算法来实现，该算法是长短期记忆（LSTM）和行动者-评论者DRL的收敛。与现有的DRL算法相比，建议的DRL既不需要任何先验知识，也不假设任何车辆网络的统计模型。此外，仿真结果表明，我们提出的智能网络切片方案的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
