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多个位于不同地点的处理单元中，已引起工业界和学术界的极大关注。然而，随着无线接入网规模持续扩大以及不同位置和时间段内资源利用率的波动，需要实施稳健的功能管理策略以最小化网络能耗。近期大多数已开发的策略忽略了服务器激活过程的激活时间及其所需能量，而这一过程会抵消服务器休眠所节省的潜在能量。此外，用户平面功能可部署于边缘计算服务器以提供低延迟服务，但尚未得到充分考量。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，旨在满足多项请求并遵守延迟和资源约束的同时，最小化能耗。在8-MEC网络中，该基于DRL的解决方案性能接近基准方案，相比现有方法可节省高达51%的能耗。在14-MEC的更大规模网络中，它仍保持38%的节能优势，并确保实时响应能力。此外，本文原型搭建了Open RAN测试平台，以验证所提方案的可行性。