9 research outputs found
Wideband cyclostationary spectrum sensing and characterization for cognitive radios
Motivated by the spectrum scarcity problem, Cognitive Radios (CRs) have been proposed as a solution to opportunistically communicate over unused spectrum licensed to Primary users (PUs). In this context, the unlicensed Secondary users (SUs) sense the spectrum to detect the presence or absence of PUs, and use the unoccupied bands without causing interference to PUs. CRs are equipped with capabilities such as, learning, adaptability, and recongurability, and are spectrum aware. Spectrum awareness comes from spectrum sensing, and it can be performed using different techniques
Interference mitigation in wideband radios using spectrum correlation and neural network
Technologies such as cognitive radio and dynamic spectrum access rely on spectrum sensing which provides wireless devices with information about the radio spectrum in the surrounding environment. One of the main challenges in wireless communications is the interference caused by malicious users on the shared spectrum. In this manuscript, an artificial intelligence enabled cognitive radio framework is proposed at system-level as part of a cyclic spectrum intelligence algorithm for interference mitigation in wideband radios. It exploits the cyclostationary feature of signals to differentiate users with different modulation schemes and an artificial neural network as classifier to detect potential malicious users. A dataset consisting of experimental modulated and dynamic signals is recorded by spectrum measurements with an in-house software defined radio testbed and then processed. Cyclostationary features are extracted for each detected signal and fed to a neural network classifier as training and testing data in a complex and dynamic scenario. Results highlight a classification rate of 1 in most of cases, even at low transmission power. A comparison with two previous works with hand-crafted features, which employ an energy detector-based classifier and a naive Bayes-based classifier, respectively, is discussed
PHY-layer Security in Cognitive Radio Networks through Learning Deep Generative Models: an AI-based approach
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
Data-Driven Approach based on Deep Learning and Probabilistic Models for PHY-Layer Security in AI-enabled Cognitive Radio IoT.
PhD Theses.Cognitive Radio Internet of Things (CR-IoT) has revolutionized almost every eld of life
and reshaped the technological world. Several tiny devices are seamlessly connected in
a CR-IoT network to perform various tasks in many applications. Nevertheless, CR-IoT
su ers from malicious attacks that pulverize communication and perturb network performance.
Therefore, recently it is envisaged to introduce higher-level Arti cial Intelligence
(AI) by incorporating Self-Awareness (SA) capabilities into CR-IoT objects to facilitate
CR-IoT networks to establish secure transmission against vicious attacks autonomously.
In this context, sub-band information from the Orthogonal Frequency Division Multiplexing
(OFDM) modulated transmission in the spectrum has been extracted from the
radio device receiver terminal, and a generalized state vector (GS) is formed containing
low dimension in-phase and quadrature components. Accordingly, a probabilistic method
based on learning a switching Dynamic Bayesian Network (DBN) from OFDM transmission
with no abnormalities has been proposed to statistically model signal behaviors
inside the CR-IoT spectrum. A Bayesian lter, Markov Jump Particle Filter (MJPF),
is implemented to perform state estimation and capture malicious attacks.
Subsequently, GS containing a higher number of subcarriers has been investigated. In
this connection, Variational autoencoders (VAE) is used as a deep learning technique
to extract features from high dimension radio signals into low dimension latent space
z, and DBN is learned based on GS containing latent space data. Afterward, to perform
state estimation and capture abnormalities in a spectrum, Adapted-Markov Jump
Particle Filter (A-MJPF) is deployed. The proposed method can capture anomaly that
appears due to either jammer attacks in transmission or cognitive devices in a network
experiencing di erent transmission sources that have not been observed previously. The
performance is assessed using the receiver
Cyclostationary-based jammer detection for wideband radios using compressed sensing and artificial neural network
open3noCognitive radio is a promising technology for frequency allocation to improve the spectrum utilization efficiency of licensed bands. However, in recent years, the attention of the researchers is focused on security issues that have to be faced by cognitive radio technology. One of the most important issues consists of radio frequency jamming attacks, where adversaries can use on-the-fly reconfigurability and learning capabilities of cognitive radios in order to devise and deploy advanced jamming tactics. Jamming attacks can noticeably affect the performance of wireless communication systems and can lead to significant overhead in terms of data re-transmission and increased power consumption. In this article, a novel compressed sensing-based jammer detection algorithm is proposed using cyclic spectral analysis and artificial neural networks for wideband cognitive radios. A wideband spectrum is considered that is composed of multiple narrowband signals. Narrowband signals can be legitimate or jamming signals. Compressed sensing is used to reduce the overhead of the analog-to-digital conversion and it allows one to estimate a wideband spectrum with sub-Nyquist rate sampling. After the signal has been estimated, the second-order statistics, namely, spectral correlation function, is computed to extract cyclic features of the wideband signal. Finally, a pre-trained artificial neural network is proposed to classify each narrowband signal as a legitimate or jamming signal. Performances of proposed algorithm are shown with Monte-Carlo simulations under different empirical setups.openNawaz, Tassadaq; Marcenaro, Lucio; Regazzoni, Carlo S.Nawaz, Tassadaq; Marcenaro, Lucio; Regazzoni, Carlo S
Abstracts on Radio Direction Finding (1899 - 1995)
The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography).
Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM.
The contents of these files are:
1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format];
2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format];
3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion