Open Radio Access Network (Open RAN) has gained tremendous attention from industry and academia with decentralized baseband functions across multiple processing units located at different places. However, the ever-expanding scope of RANs, along with fluctuations in resource utilization across different locations and timeframes, necessitates the implementation of robust function management policies to minimize network energy consumption. Most recently developed strategies neglected the activation time and the required energy for the server activation process, while this process could offset the potential energy savings gained from server hibernation. Furthermore, user plane functions, which can be deployed on edge computing servers to provide low-latency services, have not been sufficiently considered. In this paper, a multi-agent deep reinforcement learning (DRL) based function deployment algorithm, coupled with a heuristic method, has been developed to minimize energy consumption while fulfilling multiple requests and adhering to latency and resource constraints. In an 8-MEC network, the DRL-based solution approaches the performance of the benchmark while offering up to 51% energy savings compared to existing approaches. In a larger network of 14-MEC, it maintains a 38% energy-saving advantage and ensures real-time response capabilities. Furthermore, this paper prototypes an Open RAN testbed to verify the feasibility of the proposed solution.

翻译：开放无线接入网（Open RAN）通过将基带功能分散部署于位于不同地点的多个处理单元，在工业界与学术界引起了广泛关注。然而，随着无线接入网规模的持续扩展以及不同位置与时间维度上资源利用率的动态波动，亟需实施鲁棒的功管理策略以最小化网络能耗。现有最新策略大多忽略了服务器激活过程的启动时间及其所需能耗，而该过程可能抵消服务器休眠所节省的潜在能量。此外，能够部署在边缘计算服务器上以提供低延迟服务的用户面功能尚未得到充分研究。本文提出了一种基于多智能体深度强化学习（DRL）的功能部署算法，并结合启发式方法，在满足多请求、时延及资源约束的前提下最小化能耗。在8-MEC网络中，该基于DRL的方案在逼近基准性能的同时，相较于现有方法可实现最高51%的节能效果。在规模更大的14-MEC网络中，该方案仍保持38%的节能优势，并确保实时响应能力。此外，本文搭建了Open RAN原型测试平台以验证所提方案的可行性。