A Reinforcement Learning-Based PQoS Framework for V2X Networks in NS-3

The emergence of the sixth-generation mobile networks (6G) introduces massive ultra-reliable and low-latency communications (mURLLC), critical for applications like teleoperated driving, which demand ultra-high reliability and sub-50 ms latency. This paper addresses the challenge of Predictive Quality of Service (PQoS) in Vehicle-to-Everything (V2X) networks by proposing a Reinforcement Learning (RL)-based framework. Using Deep Q-Learning (DQN) and Network Simulator 3 (ns-3), we design a teleoperated driving scenario aligned with 6G requirements. Our results show that the RL framework achieves an optimal balance between Quality of Service (QoS) and Quality of Experience (QoE), outperforming baseline solutions.