Optimizing URLLC in Open RAN: A Deep Reinforcement Learning-Based Trade-off Analysis

The advent of Ultra-Reliable Low Latency Communication (URLLC) along with the emergence of Open RAN (ORAN) architectures presents unprecedented challenges and opportunities in Radio Resource Management (RRM) for next-generation communication systems. This paper presents a com- prehensive trade-off analysis of Deep Reinforcement Learning (DRL) approaches designed to enhance URLLC performance within ORAN’s flexible and dynamic framework. By investigating various DRL strategies for optimizing RRM parameters, we explore the intricate balance between reliability, latency, and the newfound adaptability afforded by the ORAN principles. Through extensive simulation results, our study compares the efficacy of different DRL models in achieving URLLC objectives in an ORAN context, highlighting the potential of DRL to navigate the complexities introduced by ORAN. The proposed study provides valuable information on the practical implementation of DRL-based RRM solutions in ORAN-enabled wireless networks. It sheds light on the benefits and challenges of integrating DRL and ORAN for URLLC enhancements. Our findings demonstrate that the proposed twin-delayed deep-deterministic policy gradient (TD3) integrated with Thompson Sampling (TS) achieves reliability levels above 99% in more than 80% of instances, outperforming baseline DRL methods in maintaining stringent URLLC reliability requirements, offering a roadmap for future research to pursue efficient, reliable, and flexible communication systems.