Investigation of Security and Spectrum Management Issues in Cognitive Radio Aided by Machine Learning

University of Technology Sydney. Faculty of Engineering and Information Technology.Cognitive Radio (CR) is an intelligent and adaptive radio and network technology that allows transceivers to sense available frequency spectrum and change its parameters, to switch to available channels (frequency bands) without interruption to other connected transceivers. It is primarily a technology to resolve spectrum scarcity problems using Dynamic Spectrum Access (DSA). The potential aspects and applications of Cognitive radio are far superior to DSA alone. CR abilities and CR reconfiguration abilities are essential components for electronic warfare (communications). It provides capabilities for developing and deploying advanced anti-jamming methods, by assisting in the development of advanced intelligent, self-reconfiguration methods to alleviate the effects of jamming. This thesis examines the effects of jamming and other attacks on Cognitive Radio Networks and provides methods and processes to overcome those effects. Cognitive Radio architecture simulation was applied so that policies and their application correlate to Cognitive Radio jamming and anti-jamming issues. Simulation is employed for testing Multi-Armed Bandit and machine learning strategies/solutions as shown by this thesis. The central part of the thesis is the mitigation of jamming outcomes on Cognitive Radio Networks by using proactive steps to increase communication robustness and contentiousness. The thesis utilizes game theory (i.e. the Multi-Armed Bandit problem) and protection using Machine Learning (ProML) design for analysing jamming behavior on Cognitive Radio systems. MAB experiment show MAB approach is effective giants random attack, whereas, the proposed machine learning has its own merits to overcome constant and reactive jamming

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Deep Learning Meets Cognitive Radio: Predicting Future Steps

Learning the channel occupancy patterns to reuse the underutilised spectrum frequencies without interfering with the incumbent is a promising approach to overcome the spectrum limitations. In this work we proposed a Deep Learning (DL) approach to learn the channel occupancy model and predict its availability in the next time slots. Our results show that the proposed DL approach outperforms existing works by 5%. We also show that our proposed DL approach predicts the availability of channels accurately for more than one time slot