La radio cognitive (CR) est une radio entièrement reconfigurable qui permet de changer intelligemment ses paramètres de communication en réponse à l’activité des autres réseaux radios et demandes d’utilisateur. L’objectif ultime de la CR est de permettre à l’utilisateur secondaire (SU) d’utiliser la ressource de spectre disponible sans interférer sur l’utilisateur primaire (PU) en utilisant des trous de spectre. Par conséquent, la détection du PU est l’un des défis principaux dans le développement de la CR. Par rapport aux systèmes conventionnels de communication sans fil, le système CR introduit de nouveaux problèmes d’allocation de ressource (RA) en raison de l’interférence des canaux adjacents utilisés par le SU et le PU. Dans le contexte de la CR, la plupart des efforts ont été menés sur les systèmes de CR basés sur le multiplexage par division de fréquences orthogonales (OFDM). Toutefois, la technique de l’OFDM montre quelques points faibles dans l’application à cause des remontées significatives du spectre. Les modulations multiporteuses à base de bancs de filtre (FBMC) ont été récemment proposées pour des applications de CR. Dans cette thèse, trois points importants pour le développement d’un système de CR basé sur le FBMC sont discutés.Les trois points principaux peuvent être résumés ainsi: nous examinons premièrement les problèmes de détection de spectre des signaux OFDM et FBMC en employant le détecteur de signature de cyclostationnarité (CS). En outre, nous proposons une architecture de détection multi-bande basée sur le banc de filtre polyphasé (PFB), et montrons son avantage; deuxièmement, la comparaison entre l’OFDM et le FBMC du point de vue de l’efficacité spectrale est discutée; et enfin, nous proposons un algorithme stratégique d’allocation de ressource pour les systèmes cognitifs multi-cellulaires et multi-utilisateurs.Les algorithmes proposés dans cette thèse ont été testés par simulation. Les résultats numériques prouvent que le FBMC, par opposition à l’OFDM, pourrait réaliser une efficacité spectrale plus élevée et offre un avantage attrayant dans la détection de spectre. Les contributions de cette thèse ont accru l’intérêt d’appliquer FBMC dans les systèmes de CR à l’avenir. / Cognitive Radio (CR) is a fully reconfigurable radio that can intelligently change its communicationvariables in response to network and user demands. The ultimate goal of CR is to allowthe Secondary User (SU) to utilize the available spectrum resource on a non-interfering basis to thePrimary User (PU) by sensing the existence of spectrum holes. Therefore, the detection of PU isone of the main challenges in the development of the CR technology. Moreover, compared to conventionalwireless communication systems, CR system poses new challenges to Resource Allocation(RA) problems because of the Cross-Channel Interference (CCI) from the adjacent channels used bySU to PU. In the CR context, most past efforts have been spent on Orthogonal Frequency DivisionMultiplexing (OFDM) based CR systems. However, OFDM technique exhibits some shortcomingsin application due to its significant spectrum leakage. Filter Bank based Multi-Carrier (FBMC), asanother promising Multi-Carrier Modulation (MCM) candidate, has been recently proposed for CRapplications. In this dissertation, three important issues in developing a FBMC based CR system arediscussed.The three prime issues can be summarized: we firstly survey the spectrum sensing problemsof OFDM and FBMC signals by using Cyclostationary Signature (CS) detector. Furthermore, wepropose a Polyphase Filter Bank (PFB) based multi-band sensing architecture, and argue for its advantage;secondly, the comparison of OFDM and FBMC from the spectral efficiency point of viewis discussed; and lastly, our emphasis is placed on the strategic resource allocation algorithms fornon-cooperative multi-cell CR systems.The overall proposed algorithms have been verified by simulation. Numerical results show thatFBMC, as opposed to OFDM, could achieve higher spectrum efficiency and attractive benefit inspectrum sensing. The contributions of this dissertation have heighten the interest in applying FBMCin the future CR systems.

Cognitive radios are the radios which use spectrum licensed to other users. For this, they perform Radio Environment Analysis, identify the Spectral holes and then operate in those holes. We consider the problem of Spectrum Sensing in Cognitive Radio Networks. Our Algorithms are based on Sequential Change Detection techniques. In this work we have used DualCUSUM, a distributed algorithm developed recently for cooperative spectrum sensing. This is used by cognitive (secondary) nodes to sense the spectrum which then send their local decisions to a fusion center. The fusion center again sequentially processes the received information to arrive at the final decision. We show that DualCUSUM performs better than all other existing spectrum sensing algorithms. We present a generalized analysis of DualCUSUM and compare the analysis with simulations to show its accuracy. DualCUSUM requires the knowledge of the channel gains for each of the secondary users and the receiver noise power. In Cognitive Radio setup it is not realistic to assume that each secondary user will have this knowledge. So later we modify DualCUSUM to develop GLRCUSUM algorithms which can work with imprecise estimates of the channel gains and receiver noise power. We show that the SNR wall problem encountered in this scenario by other detectors is not experienced by our algorithm. We also analyze the GLRCUSUM algorithms theoretically. We also apply our algorithms for detecting the presence of the primary in an Orthogonal Frequency Division Multiplexing (OFDM) setup. We first consider the Cyclic Prefix (CP) detector, which is considered to be robust to uncertainties in noise power, and further modify the CPdetector to take care of some of the common impairments like Timing offset, Frequency offset and IQ imbalance. We further modify the CPdetector to work under frequency selective channel. We also consider the energy detector under different impairments and show that the sequential detection based energy detectors outperform cyclic prefix based Detectors.

Abstract Cognitive Radio (CR) improves the efficiency of spectrum utilization by allowing non- licensed users to utilize bands when not occupied by licensed users. In this thesis, we address several challenges currently limiting the wide use of cognitive radios. These challenges include identification of unoccupied bands, energy consumption and other technical challenges. Improved accuracy edge detection techniques are developed for CR to mitigate both noise and estimation error variance effects. Next, a reduced complexity Simplified DFT (SDFT) is proposed for use in CR. Then, a sub-Nyquist rate A to D converter is introduced to reduce energy consumption. Finally, a novel multi-resolution PSD estimation based on expectation-maximization algorithm is introduced that can obtain a more accurate PSD within a specified sensing time.

One statement that we can make with absolute certainty in our current time is that wireless communication is now the standard and the de-facto type of communication. Cognitive radios are able to interpret the frequency spectrum and adapt. The aim of this work is to be able to predict whether a frequency channel is going to be busy or free in a specific time located in the future. To do this, the problem is modeled as a time series problem where each usage of a channel is treated as a sequence of busy and free slots in a fixed time frame. For this time series problem, the method being implemented is one of the latest, state-of-the-art, technique in machine learning for time series and sequence prediction: long short-term memory neural networks, or LSTMs.

The aim of master’s thesis is a classification of digital modulation type. The interest in modulation classification has been growing for last years. It has several possible roles in both civilian and military applications such as spectrum sensing, signal confirmation, interference identification, monitoring and so on. Modulation classification is an intermediate step between signal detection and successful demodulation. Therefore the known methods are based on different statistics obtained from received signals. These statistics can be derived from continuous time signals and they hold for sampled signals.

Master’s thesis belongs to the Cognitive radio network sphere. These networks utilize frequency spectrum more effectively than networks used in present radio communications. The Cognitive radio concept makes coexistence of classic and cognitive radio networks possible. Attention is aimed at spectrum sensing as the key task of the Cognitive radio. Main properties of the cyclostationary detector, as the detector, that reaches high probability of the detection at a very low signal to noise ratio with apriori knowledge of the transmitted signal's cyclic frequency, are examined in this paper. The OFDM signals, that inherit cyclostationarity from cyclic prefix, used in the real systems have been chosen for testing the properties of the detector. The influences of decimation and multipath propagation on the probability of detection are quantitatively expressed. The optimal values for the weights of the multicycle detector are determined.

The federal communications commission (FCC) has predicted that, under the current regulatory environment, a spectrum shortage may be faced in the near future. This impending spectrum shortage is in part due to a rapidly increasing demand for wireless services and in part due to inefficient usage of currently licensed bands. A new paradigm pertaining to wireless spectrum allocation, known as cognitive radio (CR), has been proposed as a potential solution to this problem. This dissertation seeks to contribute to research in the field of CR through an investigation into the effect that a primary user (PU) channel occupancy model will have on the performance of a secondary user (SU) in a CR network. The model assumes that PU channel occupancy can be described as a binary process and a two state Hidden Markov Model (HMM) was thus chosen for this investigation. Traditional algorithms for training the model were compared with certain evolutionary-based training algorithms in terms of their resulting prediction accuracy and computational complexity. The performance of this model is important since it provides SUs with a basis for channel switching and future channel allocations. A CR simulation platform was developed and the results gained illustrated the effect that the model had on channel switching and the subsequently achievable performance of a SU operating within a CR network. Performance with regard to achievable SU data throughput, PU disruption rate and SU power consumption, were examined for both theoretical test data as well as data obtained from real world spectrum measurements (taken in Pretoria, South Africa). The results show that a trade-off exists between the achievable SU throughput and the average PU disruption rate. Significant SU performance improvements were observed when prediction modelling was employed and it was found that the performance and complexity of the model were influenced by the algorithm employed to train it. SU performance was also affected by the length of the quick sensing interval employed. Results obtained from measured occupancy data were comparable with those obtained from theoretical occupancy data with an average percentage similarity score of 96% for prediction accuracy (using the Viterbi training algorithm), 90% for SU throughput, 83% for SU power consumption and 71% for PU disruption rate. / Dissertation (MEng)--University of Pretoria, 2012. / Electrical, Electronic and Computer Engineering / unrestricted

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One of the problems with the modern radio communication is the lack of availableradio frequencies. Recent studies have shown that, while the available licensed radiospectrum becomes more occupied, the assigned spectrum is significantly underutilized.To alleviate the situation, cognitive radio (CR) technology has been proposedto provide an opportunistic access to the licensed spectrum areas. Secondary CRsystems need to cyclically detect the presence of a primary user by continuouslysensing the spectrum area of interest. Radiowave propagation effects like fading andshadowing often complicate sensing of spectrum holes. When spectrum sensing isperformed in a cooperative manner, then the resulting sensing performance can beimproved and stabilized. In this thesis, two fully distributed and adaptive cooperative Primary User (PU)detection solutions for CR networks are studied. In the first part of this thesis we study a distributed energy detection schemewithout using any fusion center. Due to reduced communication such a topologyis more energy efficient. We propose the usage of distributed, diffusion least meansquare (LMS) type of power estimation algorithms with different network topologies.We analyze the resulting energy detection performance by using a commonframework and verify the theoretical findings through simulations. In the second part of this thesis we propose a fully distributed detection scheme,based on the largest eigenvalue of adaptively estimated correlation matrices, assumingthat the primary user signal is temporally correlated. Different forms of diffusionLMS algorithms are used for estimating and averaging the correlation matrices overthe CR network. The resulting detection performance is analyzed using a commonframework. In order to obtain analytic results on the detection performance, theadaptive correlation matrix estimates are approximated by a Wishart distribution.The theoretical findings are verified through simulations. / <p>QC 20170908</p>

The tremendous success of various wireless applications operating in unlicensed bands has resulted in the overcrowding of those bands. Cognitive radio (CR) is a new technology that enables an unlicensed user to coexist with incumbent users in licensed spectrum bands without inducing interference to incumbent communications. This technology can significantly alleviate the spectrum shortage problem and improve the efficiency of spectrum utilization. Networks consisting of CR nodes (i.e., CR networks)---often called dynamic spectrum access networks or NeXt Generation (XG) communication networks---are envisioned to provide high bandwidth to mobile users via heterogeneous wireless architectures and dynamic spectrum access techniques. In recent years, the operational aspects of CR networks have attracted great research interest. However, research on the security aspects of CR networks has been very limited. In this thesis, we discuss security issues that pose a serious threat to CR networks. Specifically, we focus on three potential attacks that can be launched at the physical or MAC layer of a CR network: primary user emulation (PUE) attack, spectrum sensing data falsification (SSDF) attack, and control channel jamming (CCJ) attack. These attacks can wreak havoc to the normal operation of CR networks. After identifying and analyzing the attacks, we discuss countermeasures. For PUE attacks, we propose a transmitter verification scheme for attack detection. The scheme utilizes the location information of transmitters together with their signal characteristics to verify licensed users and detect PUE attackers. For both SSDF attacks and CCJ attacks, we seek countermeasures for attack mitigation. In particular, we propose Weighted Sequential Probability Ratio Test (WSPRT) as a data fusion technique that is robust against SSDF attacks, and introduce a multiple-rendezvous cognitive MAC (MRCMAC) protocol that is robust against CCJ attacks. Using security analysis and extensive numerical results, we show that the proposed schemes can effectively counter the aforementioned attacks in CR networks. / Ph. D.