Feature Reduction Method for Cognition and Classification of IoT Devices Based on Artificial Intelligence

In recent years, the scale and application scenarios of the Internet of Things (IoT) have been expanding. Since traditional algorithms are unable to meet wireless networks computing capability requirement in the IoT, more and more research institutions and scholars have turned their eyes to artificial intelligence (AI) methods. Because the IoT device uses wireless networks to communicate in most scenarios, this paper systematically studies the method of feature dimension reduction of wireless communication signals. In this paper, we will take the power amplifier radio frequency (RF) fingerprinting as an example. Focusing on reducing the high dimensionality of RF fingerprint features and the uncorrelated or redundant features in the features space, the RF fingerprint feature dimension reduction method is mainly studied. Based on the principal component analysis (PCA), linear discriminant analysis (LDA), and auto encoder (AE) research, this paper studies the PCA-LDA method and uses the distance ratio criterion to evaluate the separability of features. The simulation results show that the classification accuracy of PCA-LDA is superior to PCA, LDA, and AE in most SNR, and the characteristics of PCA-LDA is more separable.