用自训练方法提前预测毫米波车联网信道状态，减少频繁切换
Autonomous Self-Trained Channel State Prediction Method for mmWave Vehicular Communications
- 基站收集用户反馈与广播消息，自动生成标签数据训练RNN模型
- 在deepMIMO环境中验证，预测准确率显著提升，支持多种输入特征
- 适合高移动性场景下的5G毫米波车联网系统优化
由于用户高速移动导致频繁触发波束切换，建立和维持5G毫米波车联网连接面临重大挑战。不同于依赖终端反馈的被动波束切换，主动波束切换通过提前获取精确的信道状态信息（CSI）预测来准备。本文提出一种面向毫米波车联网用户的自主自训练CSI预测框架：基站收集车辆用户反馈的CSI，并结合其广播的C-V2X协同感知消息（CAMs），构建标注数据集，用于训练基于循环神经网络（RNN）的CSI预测模型。通过deepMIMO数据集生成环境实现并评估该框架，验证了其对5G毫米波车联网用户提供高精度CSI预测的能力，并研究了不同输入特征下的预测性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。