论文标题
通过在线学习的O-RAN虚拟化基站的能源感知的调度
Energy-aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning
论文作者
论文摘要
用于配置虚拟化基站（VBS）的开放无线接入网络（O-RAN）的设计对网络运营商来说至关重要。此任务是具有挑战性的，因为优化VBS调度程序需要了解参数的知识，这些参数是不稳定且要求提前获得的。在本文中，我们提出了一种在线学习算法，以平衡VBS的性能和能耗。该算法在不可预见的条件下（例如非平稳流量和网络状态）提供了性能保证，并且忽略了VBS操作配置文件。我们以最通用的形式研究了这个问题，并证明所提出的技术在快速变化的环境中即使在零平均最佳差距上也能达到次线性遗憾（即零平均最佳差距）。通过使用现实世界数据和各种跟踪驱动的评估，我们的发现表明，与最先进的基准相比，VB的功耗最高可节省74.3％。
The design of Open Radio Access Network (O-RAN) compliant systems for configuring the virtualized Base Stations (vBSs) is of paramount importance for network operators. This task is challenging since optimizing the vBS scheduling procedure requires knowledge of parameters, which are erratic and demanding to obtain in advance. In this paper, we propose an online learning algorithm for balancing the performance and energy consumption of a vBS. This algorithm provides performance guarantees under unforeseeable conditions, such as non-stationary traffic and network state, and is oblivious to the vBS operation profile. We study the problem in its most general form and we prove that the proposed technique achieves sub-linear regret (i.e., zero average optimality gap) even in a fast-changing environment. By using real-world data and various trace-driven evaluations, our findings indicate savings of up to 74.3% in the power consumption of a vBS in comparison with state-of-the-art benchmarks.
