MIMO Beam Signature Detection for 5G based on Machine Learning

Abstract

This paper explores the application of deep learning techniques to enhance physical layer security in 5G networks by detecting spoofing attacks through the analysis of MIMO beam patterns. The study focuses on developing and evaluating three machine learning models—Deep Autoencoder (DAE), Convolutional DAE, and Convolutional Neural Networks (CNNs)—to identify unique beam signatures caused by manufacturing imperfections in antenna arrays. Using datasets generated via simulations, the models are trained to distinguish legitimate devices from intruders based on their transmission beam patterns. The performance of the models is evaluated using metrics such as accuracy, precision, recall, and specificity. The results demonstrate the potential for using neural networks to implement robust security measures in 5G networks by leveraging intrinsic physical characteristics of antennas, providing a new layer of protection against unauthorized access.