AI-based Abnormality Detection at the PHY-layer of Cognitive Radio by Learning Generative Models

Introducing a data-driven Self-Awareness (SA) module in Cognitive Radio (CR) can support the system to establish secure networks against various attacks from malicious users. Such users can manipulate the radio spectrum in order to make the CR learn wrong behaviours and take mistaken actions. A basic SA module includes the ability to learn generative models and detect abnormalities inside the radio spectrum. In this work, we propose and implement Artificial Intelligence (AI)-based Abnormality Detection techniques at the physical (PHY)-layer in CR enabled by learning Generative Models. Specifically, two real-world practical applications related to different data dimensionality and sampling rates are presented. The first application implements the Conditional Generative Adversarial Network (C-GAN) investigated on generalized state vectors extracted from spectrum representation samples to study the dynamic behaviour of the wideband signal. While the second application is based on learning a Dynamic Bayesian Network (DBN) model from a generalized state vector which contains sub-bands information extracted from the radio spectrum. Results show that both of the proposed methods are capable of detecting abnormal signals in the spectrum and pave the road towards Self-Aware radio.