SORA: Energy-Efficient Resource Allocation in Open Radio Access Network With Online Learning - Aerman Tuerxun; Akihiro Nakao (IEEE Access)
The Open Radio Access Network (O-RAN) architecture, which leverages virtualized base stations deployable on diverse commodity hardware, is poised to enhance the flexibility and cost-effectiveness of next-generation cellular networks. However, its widespread adoption faces challenges due to the complexity of configuration choices, which can significantly affect both network performance and energy efficiency. Achieving the right balance requires intelligent resource allocation—optimizing Modulation and Coding Scheme (MCS) selection and Physical Resource Block (PRB) assignment to maximize spectrum and processing efficiency. In this work, we propose a Thompson Sampling-based online learning framework for energy-efficient resource allocation in virtualized base stations. Our approach is designed to operate with minimal assumptions about the network environment, making it highly adaptable to both stationary and dynamic conditions. By integrating variational inference with Thompson Sampling, the framework efficiently balances exploration and exploitation, allowing dynamic adjustment of MCS and PRB allocations in response to changing network states. The proposed algorithm achieves sub-linear regret, ensuring convergence toward an optimal policy over time while maintaining robust adaptability. We validate our method using open-source platforms—OpenAirInterface and FlexRIC—demonstrating its practical effectiveness. Our results show that the proposed approach achieves energy savings of up to 15 percent and improves resource block utilization efficiency by up to 31 percent compared to the default scheduling policy. These findings underscore the potential of our method to significantly enhance the sustainability and efficiency of O-RAN deployments, paving the way for smarter, more adaptive next-generation cellular networks.