Towards Resilient 6G O-RAN: An Energy-Efficient URLLC Resource Allocation Framework

Abstract

The demands of ultra-reliable low-latency communication (URLLC) in “NextG” cellular networks necessitate innovative approaches for efficient resource utilization. The current literature on 6G O-RAN primarily addresses improved mobile broadband (eMBB) performance or URLLC latency optimization individually, often neglecting the intricate balance required to optimize both simultaneously under practical constraints. This paper addresses this gap by proposing a DRL-based resource allocation framework integrated with meta-learning to manage eMBB and URLLC services adaptively. Our approach efficiently allocates heterogeneous network resources, aiming to maximize energy efficiency (EE) while minimizing URLLC latency, even under varying environmental conditions. We highlight the critical importance of accurately estimating the traffic distribution flow in the multi-connectivity (MC) scenario, as its uncertainty can significantly degrade EE. The proposed framework demonstrates superior adaptability across different path loss models, outperforming traditional methods and paving the way for more resilient and efficient 6G networks.