PhD ThesisRecently, Cognitive Radio (CR) has been intended as an intelligent radio endowed with
cognition which can be developed by implementing Artificial Intelligence (AI) techniques.
Specifically, data-driven Self-Awareness (SA) functionalities, such as detection of spectrum
abnormalities, can be effectively implemented as shown by the proposed research. One important
application is PHY-layer security since it is essential to establish secure wireless communications
against external jamming attacks.
In this framework, signals are non-stationary and features from such kind of dynamic
spectrum, with multiple high sampling rate signals, are then extracted through the Stockwell
Transform (ST) with dual-resolution which has been proposed and validated in this work as
part of spectrum sensing techniques.
Afterwards, analysis of the state-of-the-art about learning dynamic models from observed
features describes theoretical aspects of Machine Learning (ML). In particular, following the
recent advances of ML, learning deep generative models with several layers of non-linear
processing has been selected as AI method for the proposed spectrum abnormality detection
in CR for a brain-inspired, data-driven SA.
In the proposed approach, the features extracted from the ST representation of the wideband
spectrum are organized in a high-dimensional generalized state vector and, then, a generative
model is learned and employed to detect any deviation from normal situations in the analysed
spectrum (abnormal signals or behaviours). Specifically, conditional GAN (C-GAN),
auxiliary classifier GAN (AC-GAN), and deep VAE have been considered as deep generative
models.
A dataset of a dynamic spectrum with multi-OFDM signals has been generated by using
the National Instruments mm-Wave Transceiver which operates at 28 GHz (central carrier frequency)
with 800 MHz frequency range. Training of the deep generative model is performed
on the generalized state vector representing the mmWave spectrum with normality pattern
without any malicious activity. Testing is based on new and independent data samples corresponding
to abnormality pattern where the moving signal follows a different behaviour which
has not been observed during training.
An abnormality indicator is measured and used for the binary classification (normality hypothesis
otherwise abnormality hypothesis), while the performance of the generative models
is evaluated and compared through ROC curves and accuracy metrics
