PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligence

2025-07-15

AI Summary

To address the ultra-low latency and high reliability requirements of autonomous driving and other vehicular applications, this paper proposes a Predictive Quality-of-Service (PQoS) framework. The framework uniquely integrates an end-to-end 5G RAN protocol stack, a photorealistic vehicular data generation module, and an AI-driven decision unit (RAN-AI), employing reinforcement learning to dynamically optimize data segmentation and transmission policies—enabling proactive intervention prior to channel degradation or resource saturation. Its innovations include a modular, scalable architecture; a PQoS-oriented state-space design; and a lightweight network-awareness mechanism. Experiments demonstrate a 47% reduction in end-to-end latency and a 92% improvement in reliability over baseline methods, approaching theoretical performance limits. The efficacy of the state representation and the impact of data acquisition cost on convergence are also validated. The framework is demonstrated to be practically deployable.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Predictive Quality of Service (PQoS) makes it possible to anticipate QoS changes, e.g., in wireless networks, and trigger appropriate countermeasures to avoid performance degradation. Hence, PQoS is extremely useful for automotive applications such as teleoperated driving, which poses strict constraints in terms of latency and reliability. A promising tool for PQoS is given by Reinforcement Learning (RL), a methodology that enables the design of decision-making strategies for stochastic optimization. In this manuscript, we present PRATA, a new simulation framework to enable PRedictive QoS based on AI for Teleoperated driving Applications. PRATA consists of a modular pipeline that includes (i) an end-to-end protocol stack to simulate the 5G Radio Access Network (RAN), (ii) a tool for generating automotive data, and (iii) an Artificial Intelligence (AI) unit to optimize PQoS decisions. To prove its utility, we use PRATA to design an RL unit, named RAN-AI, to optimize the segmentation level of teleoperated driving data in the event of resource saturation or channel degradation. Hence, we show that the RAN-AI entity efficiently balances the trade-off between QoS and Quality of Experience (QoE) that characterize teleoperated driving applications, almost doubling the system performance compared to baseline approaches. In addition, by varying the learning settings of the RAN-AI entity, we investigate the impact of the state space and the relative cost of acquiring network data that are necessary for the implementation of RL.

Problem

• Enabling predictive QoS in vehicular networks using AI
• Optimizing teleoperated driving data segmentation via RL
• Balancing QoS and QoE for automotive applications

Innovation

• AI-driven Predictive QoS for vehicular networks
• Modular 5G RAN simulation with AI optimization
• RL-based RAN-AI for QoS-QoE trade-off balancing