DRILL-Q: Delay-Responsive Intelligent Learning for Latency-sensitive QoS

Resource scheduling plays a critical role in managing congestion in modern networks. This problem is inherently complex, as it requires timely decision-making where previous allocations influence future resource availability. Traditional scheduling policies aim to guarantee Quality-of-Service (QoS) while ensuring fair resource distribution. More recently, Machine Learning (ML)-based schedulers have been proposed to enhance scheduling efficiency. However, existing solutions fail to explicitly integrate Guaranteed Flow Bit Rate (GFBR) constraints into Reinforcement Learning (RL)-based scheduling decisions. In this paper, we propose Delay-Responsive Intelligent Learning for Latency-sensitive QoS (DRILL-Q), a novel RL based, QoS-aware Medium Access Control (MAC) scheduler designed to manage multi-flow traffic while explicitly ensuring GFBR compliance. DRILL-Q leverages a reward function that incorporates Packet Delay Budget (PDB), Head-of-Line (HOL) delay, and priority levels to optimize scheduling decisions. Simulation results demonstrate that DRILL-Q successfully prioritizes traffic based on its QoS requirements, while significantly improving Jain’s fairness among flows with the same 5G QoS Identifier (5QI) values. Compared to traditional and deterministic scheduling algorithms, DRILL-Q achieves superior resource allocation, reducing delay and jitter while maintaining higher throughput under congested network conditions.