Intelligent QoS-Aware Slice Resource Allocation With User Association Parameterization for Beyond 5G O-RAN-Based Architecture Using DRL

The escalating demands of next-generation, beyond-5G services necessitate innovative approaches to dynamic resource management, critical for satisfying end-user expectations and maintaining quality of service (QoS). This study leverages the Open Radio Access Network (O-RAN) architecture's flexibility and programmability to introduce a novel deep reinforcement learning (DRL) strategy for QoS-aware intra-slice resource allocation. By employing intelligent agents and a Deep Q-Network (DQN)-based framework, our approach precisely tailors resource distribution within O-RAN, optimizing for enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) slices. The proposed method, featuring intelligent QoS-aware resource allocation (IQRA) and its low-complexity variant (LIQRA), demonstrates significant throughput improvements for eMBB by 11.5% compared to state-of-the-art (SOTA) methods and reduces URLLC latency by 19.94% and 16.54%, achieving up to 45.5% lower latency than baseline. A streamlined algorithm effectively reduces computational complexity, ensuring robust performance under resource constraints. Simulation results underscore the algorithm's ability to substantially enhance 5G network slice performance, offering a parameterized solution for user association in O-RAN networks using DRL. This research not only meets high key performance indicators (KPIs) but also advances edge intelligence, fostering a more responsive network ecosystem.