Evolving 5G-LENA Towards 6G: Integrating AI for Intelligent Scheduling of Multi-Flow Traffic

Artificial Intelligence (AI) will play a crucial role in next generation networks, where adaptability and self-optimization are essential for achieving zero-touch, autonomous network operation.As AI techniques are integrated into the architecture of next generation networks, such as 6G, having tools that will allow the application of AI for testing and experimentation is of high importance.Moreover, AI techniques are increasingly being employed to enhance the performance of applications with challenging Quality of Service (QoS) and latency requirements.In this work we present an enhanced version of the ns-3 5G-LENA simulator, extended with AI capabilities through integration with the ns3-gym module.In addition, we introduce a novel Reinforcement Learning (RL)-based resource scheduler, namely Delay-Responsive Intelligent Learning for Latency-sensitive QoS (DRILL-Q), designed to optimize resource allocation with a special focus on latency-sensitive, multi-flow traffic.Through extensive simulations we demonstrate that the proposed scheduler significantly reduces in QoS violations, while improves fairness across flows, compared to the traditional schedulers provided in 5G-LENA, the 5G NR module for the ns-3 simulator.