QoS-DRAMA: Quality of Service Aware Drl-Based Adaptive Mid-Level Resource Allocation Scheme

To address the evolving and diverse Quality of Service (QoS) demands in modern cellular networks, an imperative for a Machine Learning (ML) optimized programmable Radio Access Network (RAN) has become evident. This study focuses on the Radio Resource Management (RRM) aspect of this paradigm by introducing a configurable QoS-aware scheduling heuristic optimized through Deep Reinforcement Learning (DRL). The proposed framework dynamically optimizes its policies by weighing and combining multiple scheduling metrics to adapt to changing RAN demands. It exhibits tremendous promise by outperforming existing heuristic benchmarks while retaining increased flexibility compared to other low level scheduler approaches that utilize DRL. The findings underscore the potential of our approach as a mid-level DRL-scheduling technique, well-positioned to meet the evolving QoS demands towards 6G cellular networks.