AF Relaying Secrecy Performance Prediction for 6G Mobile Communication Networks in Industry 5.0
Abstract
Industry 5.0 has developed in full swing, and accelerated the process of the sixth-generation (6G) mobile communication. Physical layer security is important for complex 6G mobile communication networks. To process active complex events in 6G mobile cooperative networks, predicting secrecy performance in time is essential for the mobile communication quality evaluation. Using amplify-and-forward (AF) relaying, we propose a transmit antenna selection (TAS) based secrecy scheme in this article. To analyze the security of 6G mobile cooperative networks, signal-to-noise ratio of the end-to-end link is used to derive the novel expressions for secrecy outage probability (SOP). The theoretical results are confirmed by simulation results. Then, we use SOP as the important merit to evaluate the secrecy performance, and set up the dataset. To achieve secrecy performance prediction, a convolutional neural network (CNN) based SOP prediction algorithm is proposed. The designed CNN model has five convolution layers, which all use the same convolution in the padding and do not change data size. For this improved CNN structure, we adopt the idea of SqueezeNet, which belongs to the lightweight CNN. The improved CNN model can greatly reduce the parameters and network complexity on the premise of ensuring the prediction accuracy. We also examine the following state-of-the-art techniques, first, Elman, second, InceptionNet, third, deep neural network (DNN), and fourth, support vector machine methods. The proposed CNN algorithm can achieve better SOP prediction results than other existing methods. In particular, compared with DNN method, the prediction accuracy is increased by 66.7%.