Artificial intelligence (AI) and Machine Learning (ML) are considered as key enablers for realizing the full potential of fifth-generation (5G) and beyond mobile networks, particularly in the context of resource management and orchestration. In this demonstration, we consider a fully-fledged 5G mobile network and develop a multi-agent deep reinforcement learning (DRL) framework for RAN resource allocation. By leveraging local monitoring information generated by a shared gNodeB instance (gNB), each DRL agent aims to optimally allocate radio resources concerning service-specific traffic demands belonging to heterogeneous running services. We perform experiments on the deployed testbed in real-time, showing that DRL-based agents can allocate radio resources fairly while improving the overall efficiency of resource utilization and minimizing the risk of over provisioning.

翻译：人工智能（AI）和机器学习（ML）被视为实现第五代（5G）及未来移动网络全部潜力的关键使能技术，尤其是在资源管理和编排的背景下。在此演示中，我们考虑一个完整的5G移动网络，并开发了一个用于RAN资源分配的多智能体深度强化学习（DRL）框架。通过利用共享gNodeB实例（gNB）生成的本地监控信息，每个DRL智能体旨在根据属于异构运行服务的特定业务流量需求，优化分配无线电资源。我们在部署的测试平台上开展实时实验，结果表明基于DRL的智能体能够公平分配无线电资源，同时提升资源利用的整体效率并最小化过度配置的风险。