Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning

TL;DR

This paper introduces an online learning framework for resource allocation in virtualized base stations within O-RAN, optimizing throughput and energy efficiency even in unpredictable environments, with proven sub-linear regret and significant power savings.

Contribution

It presents a novel online learning algorithm combined with meta-learning for adaptive resource management in vBSs, addressing non-stationary traffic demands and environment variability.

Findings

Achieves up to 64.5% power savings compared to benchmarks.

Proves sub-linear regret, ensuring near-optimal performance over time.

Effective in non-stationary and adversarial traffic scenarios.

Abstract

Open Radio Access Network systems, with their virtualized base stations (vBSs), offer operators the benefits of increased flexibility, reduced costs, vendor diversity, and interoperability. Optimizing the allocation of resources in a vBS is challenging since it requires knowledge of the environment, (i.e., "external'' information), such as traffic demands and channel quality, which is difficult to acquire precisely over short intervals of a few seconds. To tackle this problem, we propose an online learning algorithm that balances the effective throughput and vBS energy consumption, even under unforeseeable and "challenging'' environments; for instance, non-stationary or adversarial traffic demands. We also develop a meta-learning scheme, which leverages the power of other algorithmic approaches, tailored for more "easy'' environments, and dynamically chooses the best performing one, thus enhancing the overall system's versatility and effectiveness. We prove the proposed solutions achieve sub-linear regret, providing zero average optimality gap even in challenging environments. The performance of the algorithms is evaluated with real-world data and various trace-driven evaluations, indicating savings of up to 64.5% in the power consumption of a vBS compared with state-of-the-art benchmarks.