用于支持O-RAN的5G毫米波车载网络的自主CSI预测框架
Autonomous CSI Prediction Framework for O-RAN-Enabled 5G mmWave Vehicular Networks
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中文总结 AI 辅助
针对5G毫米波车载网络因用户移动性高带来的连接挑战，提出自主且可自训练的CSI预测框架，基站通过收集标记数据集，利用独立及联邦学习训练模型，结合CSI反馈与C-V2X数据，经评估验证了框架的可行性、准确性和灵活性。
中文摘要 AI 辅助
由于用户移动性高，建立和维持5G毫米波车载连接具有挑战性，需要设计强大且高效的波束切换程序。与基于车辆用户接收的信道状态信息（CSI）反馈的反应式波束切换不同，主动式波束切换利用CSI预测为即将到来的波束切换决策提前做准备。本文中，我们为毫米波车载用户开发了一个自主且可自训练的CSI预测框架。在所提出的框架中，基站（gNB）收集并标记数据集，以独立地并使用联邦学习（FL）来训练CSI预测模型。数据集结合了从CSI反馈以及周围车辆的蜂窝车联网（C-V2X）协同感知消息（CAM）中提取的数据。该框架置于由来自DeepMIMO模拟器的现实世界移动性和CSI数据提供支持的基于机器学习和人工智能（ML/AI）的开放式无线接入网（O-RAN）应用（rApps和xApps）的背景下。详细的评估结果证明了所提出的CSI预测框架的可行性、准确性和灵活性。
英文摘要
Establishing and maintaining 5G mmWave vehicular connectivity poses a challenge due to high user mobility, requiring the design of robust and efficient beam switching procedures. Unlike reactive beam switching based on channel state information (CSI) feedback received from vehicular users, proactive beam switching exploits CSI prediction to prepare in advance for upcoming beam switching decisions. In this paper, we develop a framework for autonomous and self-trainable CSI prediction for mmWave vehicular users. In the proposed framework, base stations (gNBs) collect and label data sets to train a CSI prediction model both independently and using federated learning (FL). The data set combines data extracted from the CSI feedback and cellular vehicle-to-everything (C-V2X) cooperative awareness messages (CAMs) of surrounding vehicles. The framework is placed in the context of machine learning and artificial intelligence (ML/AI)-based Open RAN (O-RAN) applications (rApps and xApps) fed by realistic real-world mobility and CSI data from the DeepMIMO simulator. Detailed evaluation results demonstrate feasibility, accuracy, and flexibility of the proposed CSI prediction framework
