Demo: Enabling Deep Reinforcement Learning Research for Energy Saving in Open RAN

2025-01-10

Consumer Communications and Networking Conference

AI Summary

This work addresses the energy efficiency challenges posed by high-density Open RAN deployments in next-generation wireless networks by proposing a deep reinforcement learning (DRL)-based dynamic cell on/off control mechanism. We develop the first high-fidelity simulation framework that fully supports the 5G protocol stack, user mobility, handovers, and 3GPP-compliant channel models, integrating the ns-O-RAN simulator with the Gymnasium reinforcement learning environment to enable end-to-end training and evaluation of DRL agents. The proposed approach significantly improves network energy efficiency under realistic 5G scenarios. Furthermore, we open-source the complete toolkit and accompanying tutorials, establishing a reproducible and extensible benchmark platform to advance research on energy-efficient Open RAN systems.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Reinforcement Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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 upon acceptance of this paper and a tutorial for energy efficiency testing in ns-O-RAN.

Problem

• Energy Efficiency
• Open RAN
• Deep Reinforcement Learning
• 5G Networks
• Cell Activation Control

Innovation

• Deep Reinforcement Learning
• Open RAN
• Energy Efficiency
• ns-O-RAN
• 5G Network