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.

翻译：开放无线接入网系统借助虚拟化基站（vBS）为运营商带来灵活性提升、成本降低、供应商多样性及互操作性等优势。虚拟基站中的资源分配优化极具挑战性，因其需要掌握环境信息（即"外部"信息），如流量需求与信道质量，而这些信息在数秒的短时间间隔内难以精确获取。为解决该问题，我们提出一种在线学习算法，可在不可预知的"严苛"环境（如非平稳或对抗性流量需求）下平衡有效吞吐量与vBS能耗。同时，我们开发了一种元学习方案，该方案利用其他算法方法在"简易"环境中的优势能力，动态选择最优执行算法，从而提升系统整体灵活性与有效性。我们证明所提出的解决方案可实现次线性遗憾值，即使在严苛环境下也能保持平均最优性差距为零。通过真实数据及多种轨迹驱动评估对算法性能进行验证，结果表明相较于现有最优基准方案，vBS功耗最多可降低64.5%。