5G cellular networks are characterized by a servicebased architecture (SBA) where physical and virtual network functions (NFs) interact with each other. In conjunction with multi-access edge computing (MEC), 5G systems are expected to enable a wide range of advanced applications for vertical industries as well as over-the-top (OTT) service providers. Although MEC typically processes user-plane data, in this article, we exploit it to process control-plane data via the 5G network exposure function (NEF), enabling new context-aware applications. Based on cell-specific radio access network (RAN) signaling, we envision a machine learning (ML) solution that learns the user-context evolution, where the ML engine runs on a MEC host and its prediction is used to change the network setup for a given application. As an example, to address the challenging, fast-changing vehicular channel, we describe a predictive fallback mechanism for Voice Over Internet Protocol (VoIP) calls, wherein critical channel conditions are predicted to anticipate the fallback to, e.g., a traditional voice call, thus ensuring service continuity to the end user.

中文翻译：

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车载信道上的预测性互联网语音协议回退：在 5G 网络边缘使用人工智能

5G 蜂窝网络的特点是基于服务的架构 (SBA)，其中物理和虚拟网络功能 (NF) 相互交互。与多接入边缘计算 (MEC) 相结合，5G 系统有望为垂直行业以及 OTT 服务提供商提供广泛的高级应用。尽管 MEC 通常处理用户平面数据，但在本文中，我们利用它通过 5G 网络暴露功能 (NEF) 处理控制平面数据，从而实现新的上下文感知应用程序。基于特定于小区的无线电接入网络 (RAN) 信令，我们设想了一种机器学习 (ML) 解决方案，该解决方案可以学习用户上下文的演变，其中 ML 引擎在 MEC 主机上运行，​​其预测用于更改网络设置给定的应用程序。例如，为了应对挑战，