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

TL;DR

This paper introduces PRATA, a simulation framework utilizing AI and reinforcement learning to predict and optimize QoS in vehicular networks, significantly improving teleoperated driving performance under network constraints.

Contribution

PRATA is a novel modular simulation framework that integrates 5G network simulation, automotive data generation, and AI-based decision-making for predictive QoS in vehicular applications.

Findings

RAN-AI efficiently balances QoS and QoE in teleoperated driving.

System performance nearly doubles compared to baseline methods.

Impact of state space and data acquisition costs on RL effectiveness was analyzed.

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.