AI 中文总结
研究车联网中利用机器学习技术进行信道质量预测以实现主动URLLC适配，评估DNN和LSTM模型，通过结合SUMO和Sionna-RT的模拟得出基于ML的预测性能优，能增强URLLC服务可靠性和鲁棒性。
AI 中文摘要
联网自动驾驶车辆（CAV）越来越依赖5G及未来6G的超可靠低延迟通信（URLLC）服务来支持安全关键和对时间敏感的应用。由于城市车辆环境中无线链路条件变化迅速，基于未来信道条件主动调整服务参数对于维持服务连续性和可靠性至关重要。本文研究了机器学习（ML）技术在车辆URLLC场景中信道质量预测的应用。具体评估了深度神经网络（DNN）和长短期记忆（LSTM）模型来预测未来信道条件，并以最小化性能下降实现主动服务适配。分析使用了结合SUMO交通模拟器和Sionna-RT射线追踪框架的真实模拟，在从OpenStreetMap数据重建的真实城市环境中进行。结果表明，基于ML的预测显著优于仅依赖过去信道测量的方法，性能接近提前完全知晓未来信道条件的理想情况。这些发现证明了ML驱动的预测技术在增强车联网系统URLLC服务可靠性和鲁棒性方面的潜力。
英文摘要
Connected and automated vehicles (CAVs) are expected to increasingly rely on 5G and future 6G ultra-reliable and low-latency communication (URLLC) services to support safety-critical and time-sensitive applications. Since wireless link conditions can vary rapidly in urban vehicular environments, proactively adapting service parameters based on future channel conditions is essential to maintain service continuity and reliability. In this paper, we investigate the use of machine learning (ML) techniques for channel quality prediction in vehicular URLLC scenarios. Specifically, we evaluate deep neural network (DNN) and long short-term memory (LSTM) models to forecast future channel conditions and enable proactive service adaptation with minimized performance degradation. The analysis is conducted using realistic simulations combining the SUMO traffic simulator and the Sionna-RT ray-tracing framework in a real urban environment reconstructed from OpenStreetMap data. Results show that ML-based prediction significantly outperforms approaches relying solely on past channel measurements and achieves performance close to the ideal case in which future channel conditions are perfectly known in advance. These findings demonstrate the potential of ML-driven prediction techniques to enhance the reliability and robustness of URLLC services for connected vehicular systems.
CommentsInvited paper at the IEEE COINS 2026
