Title: Federated Deep Reinforcement Learning-Driven O-RAN for Automatic Multirobot Reconfiguration Show Chinese title

Title: 基于联邦深度强化学习的O-RAN驱动自动多机器人重新配置

Abstract: The rapid evolution of Industry 4.0 has led to the emergence of smart factories, where multirobot system autonomously operates to enhance productivity, reduce operational costs, and improve system adaptability. However, maintaining reliable and efficient network operations in these dynamic and complex environments requires advanced automation mechanisms. This study presents a zero-touch network platform that integrates a hierarchical Open Radio Access Network (O-RAN) architecture, enabling the seamless incorporation of advanced machine learning algorithms and dynamic management of communication and computational resources, while ensuring uninterrupted connectivity with multirobot system. Leveraging this adaptability, the platform utilizes federated deep reinforcement learning (FedDRL) to enable distributed decision-making across multiple learning agents, facilitating the adaptive parameter reconfiguration of transmitters (i.e., multirobot system) to optimize long-term system throughput and transmission energy efficiency. Simulation results demonstrate that within the proposed O-RAN-enabled zero-touch network platform, FedDRL achieves a 12% increase in system throughput, a 32% improvement in normalized average transmission energy efficiency, and a 28% reduction in average transmission energy consumption compared to baseline methods such as independent DRL. Show Chinese abstract

Abstract: 工业 4.0 的快速发展催生了智能工厂的出现，在这些工厂中，多机器人系统自主运行以提高生产率、降低运营成本并增强系统的适应性。 然而，在这些动态且复杂的环境中维持可靠且高效的网络操作需要先进的自动化机制。 本研究提出了一种零接触网络平台，该平台集成了分层开放无线接入网（O-RAN）架构，能够无缝集成先进的机器学习算法，并动态管理通信和计算资源，同时确保与多机器人系统的不间断连接。 利用这种适应性，该平台采用联邦深度强化学习（FedDRL），使多个学习代理能够实现分布式决策，从而促进发射机（即多机器人系统）的自适应参数重新配置，以优化长期系统吞吐量和传输能量效率。 仿真结果显示，在所提出的基于 O-RAN 的零接触网络平台上，与独立深度强化学习（DRL）等基线方法相比，FedDRL 在系统吞吐量上提高了 12%，归一化平均传输能量效率提升了 32%，平均传输能耗减少了 28%。