Federated Deep Reinforcement Learning-Driven O-RAN for Automatic Multirobot Reconfiguration

TL;DR

This paper introduces a federated deep reinforcement learning approach within an O-RAN framework to autonomously optimize multirobot communication systems, significantly enhancing throughput and energy efficiency in smart factory environments.

Contribution

It presents a novel integration of FedDRL with O-RAN architecture for automated multirobot network reconfiguration, improving system performance and energy efficiency.

Findings

12% increase in system throughput

32% improvement in energy efficiency

28% reduction in energy consumption

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.