Proactive URLLC Adaptation for Connected Vehicles Through ML-Based Channel Prediction

2026-07-15

AI Summary

This study addresses the challenge of maintaining ultra-reliable low-latency communication (URLLC) in urban vehicular networks, where rapid time-variation of wireless channels undermines continuity and reliability. To overcome this, the authors propose a deep learning–based channel state prediction method that leverages a high-fidelity urban dataset generated by integrating SUMO traffic simulation with Sionna-RT ray tracing. By employing deep neural networks (DNNs) and long short-term memory (LSTM) architectures, the approach accurately forecasts future channel states. This work represents the first深度融合 of deep learning–driven channel prediction with realistic traffic dynamics and electromagnetic propagation modeling. The proposed method significantly outperforms baseline schemes relying solely on historical measurements and achieves URLLC performance approaching that under ideal channel knowledge in real-world urban scenarios, thereby enabling proactive optimization of communication parameters.

Abstract

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.

Problem

• URLLC
• connected vehicles
• channel prediction
• proactive adaptation
• wireless reliability

Innovation

• machine learning
• URLLC
• channel prediction
• connected vehicles
• proactive adaptation