Federated Reinforcement Learning-Based Resource Allocation for D2D-Aided Digital Twin Edge Networks in 6G Industrial IoT

Abstract

The 6G is conceived to address the expected high level of requirements (such as ultra-high data transmission rate, support for the highest moving speed and seamless connection, etc.) in the next decade and beyond. In the context of 6G, a large number of Industrial Internet of Things (IIoT) devices may access the network, and thanks to the rapid development of artificial intelligence make smart manufacturing has the opportunity to be realized. However, a large number of IoT devices, the tremendous volume of data, the heterogeneous nature of devices, and the increasing concerns of privacy challenge the efficient management and quality of services in IIoT. To address these problems, in this article, a device-to-device (D2D) communication-aided digital twin edge network is proposed, where edge computing is introduced to bring computing and storage resources near to the end devices, and digital twin is utilized to fill the gap between physical and virtual space and D2D communication is applied to assist resource limited IoT devices to achieve normal communication. Moreover, digital twin-empowered federated reinforcement learning is leveraged to provide privacy awareness and decentralized resource allocation strategy training on D2D communication links to further improve network performance. The simulation results show that the proposal achieves significant network performance compared with baseline algorithms.