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

Machine Learning Tips and Tricks for Power Line Communications

4openopenTonello A.M.; Letizia N.A.; Righini D.; Marcuzzi F.Tonello, A. M.; Letizia, N. A.; Righini, D.; Marcuzzi, F

Strategies for Devising Automatic Signal Recognition Algorithms in a Shared Radio Environment

In an increasingly congested and complex radio environment interference is to be expected, which poses problems for Automatic Signal Recognition (ASR) systems. This thesis explores strategies for improving ASR performance in the presence of interference. The thesis breaks the overall research question down into a number of subquestions and explores each of these in turn. A Phase-symmetric Cross Recurrence Plot is developed and used to show how a radio signal can be manipulated to separate information about the modulation from the information being carried. The Logarithmic Cyclic frequency Domain Profile is introduced to illustrate how a logarithmic representation can be used for analysing mixtures of signals with very different cyclic frequencies. After defining a canonical ASR system architecture, the concepts of an Ideal Feature and Interference Selectivity are introduced and applied to typical features used in ASR processing. Finally it is shown how these algorithmic developments can be combined in a Bayesian chain implementation that can accommodate a wide variety of feature extraction algorithms. It is concluded that future ASR systems will require features that can handle a wide range of signal types with much higher levels of interference selectivity if they are to achieve acceptable performance in shared spectrum bands. Intelligent segmentation is shown to be a requirement for future ASR systems unless features can be developed that have near ideal performance

Cognitive Radio Systems

Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems

Radar sensing for ambient assisted living application with artificial intelligence

In a time characterized by rapid technological advancements and a noticeable trend towards an older average population, the need for automated systems to monitor movements and actions has become increasingly important. This thesis delves into the application of radar, specifically Frequency Modulated Continuous Wave (FMCW) radar, as an emerging and effective sensor in the field of "Activity Recognition." This area involves capturing motion data through sensors and integrating it with machine learning algorithms to autonomously classify human activities. Radar is distinguished by its ability to accurately track complex bodily movements while ensuring privacy compliance. The research provides an in-depth examination of FMCW radar, detailing its operational principles and exploring radar information domains such as range-time and micro-Doppler signatures. Following this, the thesis presents a state-of-the-art review in activity recognition, discussing key papers and significant works that have shaped the field. The thesis then focuses on research topics where contributions were made. The first topic is human activity recognition (HAR) with different physiology, presenting a comprehensive experimental setup with radar sensors to capture various human activities. The analysis of classification results reveals the effectiveness of different radar representations. Advancing into the domain of resource-constrained system platforms. It introduces adaptive thresholding for efficient data processing and discusses the optimization of these methods using artificial intelligence, particularly focusing on the evolution algorithm such as Self-Adaptive Differential Evolution Algorithm (SADEA). The final chapter discusses the use of Long Short-Term Memory (LSTM) networks for short-range personnel recognition using radar signals. It details the training and testing methodologies and provides an analysis of LSTM networks performance in temporal classification tasks. Overall, this thesis demonstrates the effectiveness of merging radar technology with machine learning in HAR, particularly in assisted living. It contributes to the field by introducing methods optimized for resource-limited settings and innovative approaches in temporal classification using LSTM networks

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Journal of Telecommunications and Information Technology

kwartalni

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis