Abstract
Establishing and maintaining 5G mmWave vehicular connectivity poses a significant challenge due to high user mobility that necessitates frequent triggering of beam switching procedures. Departing from reactive beam switching based on the user device channel state feedback, proactive beam switching prepares in advance for upcoming beam switching decisions by exploiting accurate channel state information (CSI) prediction. In this paper, we develop a framework for autonomous self-trained CSI prediction for mmWave vehicular users where a base station (gNB) collects and labels a dataset that it uses for training recurrent neural network (RNN)-based CSI prediction model. The proposed framework exploits the CSI feedback from vehicular users combined with overhearing the C-V2X cooperative awareness messages (CAMs) they broadcast. We implement and evaluate the proposed framework using deepMIMO dataset generation environment and demonstrate its capability to provide accurate CSI prediction for 5G mmWave vehicular users. CSI prediction model is trained and its capability to provide accurate CSI predictions from various input features are investigated.
Abstract (translated)
建立和维护5G mmWave车辆连接 poses a significant challenge due to high user mobility that necessitates frequent triggering of beam switching procedures. 离开基于用户设备信道状态反馈的反应式波切换,主动波切换在事先利用准确的信道状态信息(CSI)预测进行波切换决策方面做好准备。在本文中,我们为mmWave车辆用户开发了一个自适应的CSI预测框架,该框架基于基站(gNB)收集和标记用于训练基于循环神经网络(RNN)的CSI预测模型的数据集。所提出的框架利用车辆用户产生的CSI反馈以及监听他们广播的C-V2X合作意识消息(CAMs)。我们使用 deepMIMO 数据生成环境实现并评估所提出的框架,并证明了它为5G mmWave车辆用户提供准确CSI预测的能力。我们研究了CSI预测模型的训练及其从各种输入特征提供准确CSI预测的能力。
URL
https://arxiv.org/abs/2410.02326