AI-Driven Intent-based Layer 2 Time Scheduler

Resource allocation is a fundamental problem in telecom and various other fields. In this work, we consider the issue of scheduling users for efficient radio resource management. Existing scheduling methods focus on maximizing the QoS of users. However, in future generation networks, it is imperative to schedule based on the requirements set forth by application owners. This paper presents a novel approach to time scheduling in 5G networks using model-free Reinforcement Learning (RL). The proposed framework leverages recent advancements in RL-based scheduling to dynamically allocate resources based on real-time network conditions and user-defined requirements (as defined by intents). To reduce the complexity of the action space, we introduce a novel greedy mapping function to convert the small action space of the RL agent into time-scheduling weights. Simulation results show that our approach effectively meets performance goals for throughput and latency, while reducing computational costs by 50% compared to existing methods.