The growing performance demands and higher deployment densities of next-generation wireless systems emphasize the importance of adopting strategies to manage the energy efficiency of mobile networks. In this demo, we showcase a framework that enables research on Deep Reinforcement Learning (DRL) techniques for improving the energy efficiency of intelligent and programmable Open Radio Access Network (RAN) systems. Using the open-source simulator ns-O-RAN and the reinforcement learning environment Gymnasium, the framework enables to train and evaluate DRL agents that dynamically control the activation and deactivation of cells in a 5G network. We show how to collect data for training and evaluate the impact of DRL on energy efficiency in a realistic 5G network scenario, including users' mobility and handovers, a full protocol stack, and 3rd Generation Partnership Project (3GPP)-compliant channel models. The tool will be open-sourced and a tutorial for energy efficiency testing in ns-O-RAN.

翻译：下一代无线系统日益增长的性能需求和更高的部署密度凸显了采用策略管理移动网络能效的重要性。在本演示中，我们展示了一个支持深度强化学习（DRL）技术研究的框架，旨在提升智能可编程开放无线接入网（Open RAN）系统的能效。该框架利用开源模拟器ns-O-RAN和强化学习环境Gymnasium，能够训练和评估可动态控制5G网络中小区激活与休眠的DRL智能体。我们展示了如何收集训练数据，并在一个包含用户移动性与切换、完整协议栈以及符合第三代合作伙伴计划（3GPP）标准的信道模型的真实5G网络场景中，评估DRL对能效的影响。该工具将开源，并附带用于在ns-O-RAN中进行能效测试的教程。