用开放无线网络与深度学习，让联网车高速移动时少掉线、不卡顿。
Open RAN-Enabled Deep Learning-Assisted Mobility Management for Connected Vehicles
- 基于Open RAN平台部署深度学习决策模块，提前预判切换基站
- 实测显示延迟更低，视频流和远程升级服务更稳定
- 适合高移动性车联网场景，如城市快速路自动驾驶
联网车（CVs）可借助5G及未来6G/下一代网络的特性提升智能交通系统（ITS）服务。然而，即便在蜂窝网络演进背景下，车辆在高速移动时仍可能因频繁切换基站（即切换握手，HO）导致通信中断。本文提出采用开放无线接入网（Open RAN/O-RAN）与深度学习模型相结合的决策机制，以防止因切换造成的服务质量（QoS）下降，并保障车联网服务的及时连通性。方案基于由O-RAN联盟与Linux基金会合作开发的O-RAN软件社区（OSC）开源平台，构建在近实时编排器（RIC）中运行的xApps。为验证有效性，搭建了集成OMNeT++仿真器与OSC的联合框架，使用真实城市场景数据集进行评估，涵盖视频流传输与空中下载（OTA）更新。结果表明，该方案性能优于标准3GPP切换流程，显著降低延迟。
原文摘要 · Abstract (English)
Connected Vehicles (CVs) can leverage the unique features of 5G and future 6G/NextG networks to enhance Intelligent Transportation System (ITS) services. However, even with advancements in cellular network generations, CV applications may experience communication interruptions in high-mobility scenarios due to frequent changes of serving base station, also known as handovers (HOs). This paper proposes the adoption of Open Radio Access Network (Open RAN/O-RAN) and deep learning models for decision-making to prevent Quality of Service (QoS) degradation due to HOs and to ensure the timely connectivity needed for CV services. The solution utilizes the O-RAN Software Community (OSC), an open-source O-RAN platform developed by the collaboration between the O-RAN Alliance and Linux Foundation, to develop xApps that are executed in the near-Real-Time RIC of OSC. To demonstrate the proposal's effectiveness, an integrated framework combining the OMNeT++ simulator and OSC was created. Evaluations used real-world datasets in urban application scenarios, such as video streaming transmission and over-the-air (OTA) updates. Results indicate that the proposal achieved superior performance and reduced latency compared to the standard 3GPP HO procedure.
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