Multi-attribute weighted convolutional attention neural network for multiuser physical layer authentication in IIoT

Abstract

Compared with upper layer authentication, physical layer authentication (PLA) is essential in unmanned Industrial Internet of Things (IIoT) scenarios, owing to its low complexity and lightweight. However, in dynamic environments, as the amount of users expands, the accuracy of single-attribute-based authentication decreases drastically, which becomes an urgent issue for IIoT. Accordingly, this paper proposes a novel multi-attribute-based convolutional attention neural network (CANN) for multiuser PLA. Using characteristics such as amplitude, phase, and delay, the multiple attributes from a real industrial scene are first constructed into three-dimensional matrices fed into CANN. Then, attention blocks are designed to learn the correlation between attributes and extract the attribute parts that are more instrumental in the CANN to improve authentication accuracy. In addition, to avoid confusing multiple users, a center confidence loss is introduced, which adaptively adjusts the weight of the center loss and works together with the softmax loss to train the CANN. The effectiveness of the proposed CANN-based multiuser PLA and center confidence loss is supported by experimental results. Compared with the recently proposed latent perturbed convolutional neural network (LPCNN), the CANN-based scheme improves the authentication accuracy by 8.11%, which is superior to the existing learning-based approaches. As the CANN is further trained with the loss function that combines center confidence loss, the authentication accuracy can be improved by at least 2.22%.