One of the fundamental hurdles in realizing new spectrum sharing allocation policies is that of reliable spectrum sensing. In this thesis, three research thrusts are presented in order to further research in this critical area. The first of these research thrusts is the development of a novel asynchronous and noncoherent modulation classifier for PSK/QAM modulated signals in flat-fading channels. In developing this classifier, a novel estimator for the unknown channel gain and fractional time delay is proposed which uses a method-of-moments based estimation approach. For the second research thrust of this thesis, the developed method-of-moments based estimation approach is extended to estimate the signal-to-noise ratio of PSK/QAM modulated signals in flat-fading channels, in which no a priori knowledge of the modulation format and channel parameters is assumed. Finally, in the third research thrust, a distributed spectrum sensing approach is proposed in which a network of radios collaboratively detects the presence, as well as the modulation scheme, of a signal through the use of a combination of cyclic spectrum feature-based signal classification and an iterative algorithm for optimal data fusion. / Master of Science

No / Spectrum sensing is used in cognitive radio systems to detect the availability of spectrum holes for secondary usage. The simplest and most famous spectrum sensing techniques are based either on energy detection or eigenspace analysis from Random Matrix Theory (RMT) such as using the Marchenko-Pastur law. These schemes suffer from uncertainty in estimating the noise variance which reduces their performance. In this paper we propose a new method to evaluate the noise variance that can eliminate the limitations of the aforementioned schemes. This method estimates the noise variance from a measurement set of noisy signals or noise-only signals. Extensive simulations show that the proposed method performs well in estimating the noise variance. Its performance greatly improves with increasing numbers of measurements and also with increasing numbers of samples taken per measurement.

No / Dynamic Spectrum Access (DSA) for secondary usage of underutilized radio spectrum is currently of great interest for radio regulatory authorities and for cellular network operators. However, the co-existence of multiple devices operating in the same bands, such as wireless microphones which also operate in TV bands, poses a challenge to DSA. Efficient and proactive spectrum sensing could prevent harmful interference between collocated devices, but existing blind spectrum sensing schemes such as energy detection and schemes based on Random Matrix Theory (RMT) have performance limitations. We propose a new blind spectrum sensing scheme for cognitive radio. The proposed scheme uses a new formula for the estimation of noise variance. The scheme has been evaluated through extensive simulations on wireless microphone signals and shows higher performance as compared to energy detection and RMT-based sensing schemes such as MME and EME. It also shows higher performance in terms of probability of detection (Pd).

Recently, automatic modulation classification techniques using deep neural networks on raw IQ samples have been investigated and show promise when compared to more traditional likelihood-based or feature-based techniques. While likelihood-based and feature-based techniques are effective, making classification decisions directly on the raw IQ samples removes the need for expertly crafted transformations and feature extractions. In practice, RF environments are typically very dense, and a receiver must first detect and isolate each signal of interest before classification can be performed. The errors introduced by this detection and isolation process will affect the accuracy of deep neural networks making automatic modulation classification decisions directly on raw IQ samples. The importance of defining upper limits on estimation errors in a detector is highlighted, and the negative effects of over-estimating or under-estimating these limits is explored. Additionally, to date, most of the published research has focused on synthetically generated data. While large amounts of synthetically generated data is generally much easier to obtain than real-world signal data, it requires expert knowledge and accurate models of the real world, which may not always be realistic. The experiments conducted in this work show how augmented real-world signal captures can be successfully used for training neural networks used in automatic modulation classification on raw IQ samples. It is shown that the quality and duration of real world signal captures is extremely important when creating training datasets, and that signal captures made from a single transmitter with one receiver can be broadly applicable to other radios through dataset augmentation. / Master of Science / With the increasing prevalence of wireless devices in every day life, communicating between them can become more difficult because the devices must contend with each other to send and receive information. Being able to communicate in a variety of environments can be challenging and, while devices can be pre-configured for certain situations, devices that are able to automatically adjust how they communicate are more reliable and robust. The research presented in this thesis will contribute to solving this challenge by considering machine-learning based, radio frequency signal processing algorithms that are able to automatically group different communication signals. Being able to automatically group different signals is helpful because it can provide information about the wireless environment, allowing a device to make intelligent decisions based on what it detects is happening around it. However, before these algorithms can be successfully used in wireless devices, their limitations must be better understood. To this end, the work in this thesis will show how sensitive these algorithms are to imperfections in wireless devices. This work will also show how information from new environments can be captured and manipulated to allow these algorithms to scale for unseen environments and communication signals.

Opportunistic use of unused spectrum could efficiently be carried out using the paradigm of Cognitive Radio (CR). A spectrum remains idle when the primary user (licensee) is not using it. The secondary nodes detect this spectral hole quickly and make use of it for data transmission during this interval and stop transmitting once the primary starts transmitting. Detection of spectral holes by the secondary is called spectrum sensing in the CR scenario. Spectrum Sensing is formulated as a hypothesis testing problem wherein under H0 the spectrum is free and under H1, occupied. The samples will have different probability distributions, P0 and P1, under H0 and H1 respectively. In the first part of the thesis, a new algorithm - entropy test is presented, which performs better than the available algorithms when P0 is known but not P1. This is extended to a distributed setting as well, in which different secondary nodes collect samples independently and send their decisions to a Fusion Centre (FC) over a noisy MAC which then makes the final decision. The asymptotic optimality of the algorithm is also shown. In the second part, the spectrum sensing problem under impediments such as fading, electromagnetic interference and outliers is tackled. Here the detector does not possess full knowledge of either P0 or P1. This is a more general and practically relevant setting. It is found that a recently developed algorithm (which we call random walk test) under suitable modifications works well. The performance of the algorithm theoretically and via simulations is shown. The same algorithm is extended to the distributed setting as above.

Face à une demande sans cesse croissante de haut débit et d’adaptabilité des systèmes existants, qui à son tour se traduit par l’encombrement du spectre, le développement de nouvelles solutions dans le domaine des communications sans fil devient nécessaire afin de répondre aux exigences des applications émergentes. Parmi les innovations récentes dans ce domaine, l’ultra large bande (UWB) a suscité un vif intérêt. La radio impulsionnelle UWB (IR-UWB), qui est une solution intéressante pour réaliser des systèmes UWB, est caractérisée par la transmission des impulsions de très courte durée, occupant une largeur de bande allant jusqu’à 7,5 GHz, avec une densité spectrale de puissance extrêmement faible. Cette largeur de bande importante permet de réaliser plusieurs fonctionnalités intéressantes, telles que l’implémentation à faible complexité et à coût réduit, la possibilité de se superposer aux systèmes à bande étroite, la diversité spatiale et la localisation très précise de l’ordre centimétrique, en raison de la résolution temporelle très fine.Dans cette thèse, nous examinons certains éléments clés dans la réalisation d'un système IR-UWB intelligent. Nous avons tout d’abord proposé le concept de radio UWB cognitive à partir des similarités existantes entre l'IR-UWB et la radio cognitive. Dans sa définition la plus simple, un tel système est conscient de son environnement et s'y adapte intelligemment. Ainsi, nous avons tout d’abord focalisé notre recherché sur l’analyse de la disponibilité des ressources spectrales (spectrum sensing) et la conception d’une forme d’onde UWB adaptative, considérées comme deux étapes importantes dans la réalisation d'une radio cognitive UWB. Les algorithmes de spectrum sensing devraient fonctionner avec un minimum de connaissances a priori et détecter rapidement les utilisateurs primaires. Nous avons donc développé de tels algorithmes utilisant des résultats récents sur la théorie des matrices aléatoires, qui sont capables de fournir de bonnes performances, avec un petit nombre d'échantillons. Ensuite, nous avons proposé une méthode de conception de la forme d'onde UWB, vue comme une superposition de fonctions B-splines, dont les coefficients de pondération sont optimisés par des algorithmes génétiques. Il en résulte une forme d'onde UWB qui est spectralement efficace et peut s’adapter pour intégrer les contraintes liées à la radio cognitive. Dans la 2ème partie de cette thèse, nous nous sommes attaqués à deux autres problématiques importantes pour le fonctionnement des systèmes UWB, à savoir la synchronisation et l’estimation du canal UWB, qui est très dense en trajets multiples. Ainsi, nous avons proposé plusieurs algorithmes de synchronisation, de faible complexité et sans séquence d’apprentissage, pour les modulations BPSK et PSM, en exploitant l'orthogonalité des formes d'onde UWB ou la cyclostationnarité inhérente à la signalisation IR-UWB. Enfin, nous avons travaillé sur l'estimation du canal UWB, qui est un élément critique pour les récepteurs Rake cohérents. Ainsi, nous avons proposé une méthode d’estimation du canal basée sur une combinaison de deux approches complémentaires, le maximum de vraisemblance et la décomposition en sous-espaces orthogonaux,d’améliorer globalement les performances. / Faced with an ever increasing demand of high data-rates and improved adaptability among existing systems, which inturn is resulting in spectrum scarcity, the development of new radio solutions becomes mandatory in order to answer the requirements of these emergent applications. Among the recent innovations in the field of wireless communications,ultra wideband (UWB) has generated significant interest. Impulse based UWB (IR-UWB) is one attractive way of realizing UWB systems, which is characterized by the transmission of sub nanoseconds UWB pulses, occupying a band width up to 7.5 GHz with extremely low power density. This large band width results in several captivating features such as low-complexity low-cost transceiver, ability to overlay existing narrowband systems, ample multipath diversity, and precise ranging at centimeter level due to extremely fine temporal resolution.In this PhD dissertation, we investigate some of the key elements in the realization of an intelligent time-hopping based IR-UWB system. Due to striking resemblance of IR-UWB inherent features with cognitive radio (CR) requirements, acognitive UWB based system is first studied. A CR in its simplest form can be described as a radio, which is aware ofits surroundings and adapts intelligently. As sensing the environment for the availability of resources and then consequently adapting radio’s internal parameters to exploit them opportunistically constitute the major blocks of any CR, we first focus on robust spectrum sensing algorithms and the design of adaptive UWB waveforms for realizing a cognitive UWB radio. The spectrum sensing module needs to function with minimum a-priori knowledge available about the operating characteristics and detect the primary users as quickly as possible. Keeping this in mind, we develop several spectrum sensing algorithms invoking recent results on the random matrix theory, which can provide efficient performance with a few number of samples. Next, we design the UWB waveform using a linear combination of Bsp lines with weight coefficients being optimized by genetic algorithms. This results in a UWB waveform that is spectrally efficient and at the same time adaptable to incorporate the cognitive radio requirements. In the 2nd part of this thesis, some research challenges related to signal processing in UWB systems, namely synchronization and dense multipath channel estimation are addressed. Several low-complexity non-data-aided (NDA) synchronization algorithms are proposed for BPSK and PSM modulations, exploiting either the orthogonality of UWB waveforms or theinherent cyclostationarity of IR-UWB signaling. Finally, we look into the channel estimation problem in UWB, whichis very demanding due to particular nature of UWB channels and at the same time very critical for the coherent Rake receivers. A method based on a joint maximum-likelihood (ML) and orthogonal subspace (OS) approaches is proposed which exhibits improved performance than both of these methods individually.

Cognitive radios are currently presented as the solution to the ever-increasing spectrum shortage problem. However, their increased capabilities over traditional radios introduce a new dimension of security threats. Cooperative Spectrum Sensing (CSS) has been proposed as a means to protect cognitive radio networks from the well known security threats: Primary User Emulation (PUE) and Spectrum Sensing Data Falsification (SSDF). I demonstrate a new threat to trust-based CSS protocols, called the Rogue Signal Framing (RSF) intrusion. Rogue signals can be exploited to create the illusion of malicious sensors which leads to the framing of innocent sensors and consequently, their removal from the shared spectrum sensing. Ultimately, with fewer sensors working together, the spectrum sensing is less robust for making correct spectrum access decisions. The simulation experiments illustrate the impact of RSF intrusions which, in severe cases, shows roughly 40\% of sensors removed. To mitigate the RSF intrusion's damage to the network's trust, I introduce a new defense based on community detection from analyzing the network's Received Signal Strength (RSS) diversity. Tests show a 95\% damage reduction in terms of removed sensors from the shared spectrum sensing, thus retaining the benefits of CSS protocols.

The rapid growth of the Internet of Things (IoT) introduces an additional challenge to the existing spectrum under-utilisation problem as large scale deployments of thousands devices are expected to require wireless connectivity. Dynamic Spectrum Access (DSA) has been proposed as a means of improving the spectrum utilisation of wireless systems. Based on the Cognitive Radio (CR) paradigm, DSA enables unlicensed spectrum users to sense their spectral environment and adapt their operational parameters to opportunistically access any temporally unoccupied bands without causing interference to the primary spectrum users. In the same context, CR inspired Machine-to-Machine (M2M) communications have recently been proposed as a potential solution to the spectrum utilisation problem, which has been driven by the ever increasing number of interconnected devices. M2M communications introduce new challenges for CR in terms of operational environments and design requirements. With spectrum sensing being the key function for CR, this thesis investigates the performance of spectrum sensing and proposes novel sensing approaches and models to address the sensing problem for cognitive M2M deployments. In this thesis, the behaviour of Energy Detection (ED) spectrum sensing for cognitive M2M nodes is modelled using the two-wave with dffi use power fading model. This channel model can describe a variety of realistic fading conditions including worse than Rayleigh scenarios that are expected to occur within the operational environments of cognitive M2M communication systems. The results suggest that ED based spectrum sensing fails to meet the sensing requirements over worse than Rayleigh conditions and consequently requires the signal-to-noise ratio (SNR) to be increased by up to 137%. However, by employing appropriate diversity and node cooperation techniques, the sensing performance can be improved by up to 11.5dB in terms of the required SNR. These results are particularly useful in analysing the eff ects of severe fading in cognitive M2M systems and thus they can be used to design effi cient CR transceivers and to quantify the trade-o s between detection performance and energy e fficiency. A novel predictive spectrum sensing scheme that exploits historical data of past sensing events to predict channel occupancy is proposed and analysed. This approach allows CR terminals to sense only the channels that are predicted to be unoccupied rather than the whole band of interest. Based on this approach, a spectrum occupancy predictor is developed and experimentally validated. The proposed scheme achieves a prediction accuracy of up to 93% which in turn can lead to up to 84% reduction of the spectrum sensing cost. Furthermore, a novel probabilistic model for describing the channel availability in both the vertical and horizontal polarisations is developed. The proposed model is validated based on a measurement campaign for operational scenarios where CR terminals may change their polarisation during their operation. A Gaussian approximation is used to model the empirical channel availability data with more than 95% confi dence bounds. The proposed model can be used as a means of improving spectrum sensing performance by using statistical knowledge on the primary users occupancy pattern.

Em 1997, o conceito de rádio cognitivo foi proposto pela primeira vez e evoluiu significativamente até os dias de hoje, como solução para o problema da escassez de espectro eletromagnético. Nessa proposta, usuários oportunistas, através de acesso dinâmico ao espectro, fazem uso das faixas de frequências atribuídas a usuários licenciados, quando eles não as estão utilizando. Para que isso seja possível, sem interferir ou degradar os sinais dos usuários licenciados, é necessário atender a quatro requisitos essenciais de rádios cognitivos: Sensoriamento espectral, Decisão do espectro, Compartilhamento do espectro e Mobilidade espectral. Neste trabalho, o sensoriamento espectral é investigado com base na detecção de energia. Um algoritmo é desenvolvido para se determinar o número de canais ocupados e o número de amostras necessárias na detecção para se atingir probabilidades de detecção e falso alarme pré-estabelecidas. Resultados de simulações são apresentadas mostrando que a incerteza do ruído degrada o desempenho do sistema quando a relação sinal-ruído é baixa. O algoritmo desenvolvido permite também determinar o limite inferior para a relação sinal-ruído, quando há incerteza do ruído. O comportamento da probabilidade de detecção em função da probabilidade de falso alarme parametrizado para número de amostras e relação sinal-ruído é apresentado. As curvas resultantes são muitas vezes referidas como curvas ROC - Receiver Operation Characteristics na literatura. Em função do grande interesse sócio-político pela banda de TV, que o cenário das telecomunicações atualmente apresenta, a mesma foi escolhida para alguns exemplos deste estudo. / In 1997, the concept of cognitive radio was proposed for the first time and evolved significantly to the present days, as a solution to the problem of electromagnetic spectrum scarcity. In the proposed approach, opportunistic users utilize frequency bands originally assigned to licensed users through dynamic spectrum access when the licensed users are not using them. To make this possible, without interfering or degrading the signals from the licensed users, it is necessary to fulfill four essential requirements of cognitive radios: spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility. In this work, spectrum sensing based on energy detection was investigated. An algorithm was developed for the determination of channel occupation and the number of samples needed for the detection process to achieve pre-established probabilities of detection and false-alarm. Simulations results are presented showing that noise uncertainty degrade the performance of the system when the signal-to-noise ratio is low. The developed algorithm allows determining a lower threshold for the signal-to-noise ratio, when noise uncertainty exists. The detection probability behavior as a function of the false alarm probability having the number of samples and the signal-to noise ratio as parameters is presented. The resulting curves are often denominated ROC - Receiver Operation Characteristics in the literature. Due to the high social and political interest in the TV broadcasting band, that telecommunications scenario currently presents, this band was chosen for same examples in this study.

Em 1997, o conceito de rádio cognitivo foi proposto pela primeira vez e evoluiu significativamente até os dias de hoje, como solução para o problema da escassez de espectro eletromagnético. Nessa proposta, usuários oportunistas, através de acesso dinâmico ao espectro, fazem uso das faixas de frequências atribuídas a usuários licenciados, quando eles não as estão utilizando. Para que isso seja possível, sem interferir ou degradar os sinais dos usuários licenciados, é necessário atender a quatro requisitos essenciais de rádios cognitivos: Sensoriamento espectral, Decisão do espectro, Compartilhamento do espectro e Mobilidade espectral. Neste trabalho, o sensoriamento espectral é investigado com base na detecção de energia. Um algoritmo é desenvolvido para se determinar o número de canais ocupados e o número de amostras necessárias na detecção para se atingir probabilidades de detecção e falso alarme pré-estabelecidas. Resultados de simulações são apresentadas mostrando que a incerteza do ruído degrada o desempenho do sistema quando a relação sinal-ruído é baixa. O algoritmo desenvolvido permite também determinar o limite inferior para a relação sinal-ruído, quando há incerteza do ruído. O comportamento da probabilidade de detecção em função da probabilidade de falso alarme parametrizado para número de amostras e relação sinal-ruído é apresentado. As curvas resultantes são muitas vezes referidas como curvas ROC - Receiver Operation Characteristics na literatura. Em função do grande interesse sócio-político pela banda de TV, que o cenário das telecomunicações atualmente apresenta, a mesma foi escolhida para alguns exemplos deste estudo. / In 1997, the concept of cognitive radio was proposed for the first time and evolved significantly to the present days, as a solution to the problem of electromagnetic spectrum scarcity. In the proposed approach, opportunistic users utilize frequency bands originally assigned to licensed users through dynamic spectrum access when the licensed users are not using them. To make this possible, without interfering or degrading the signals from the licensed users, it is necessary to fulfill four essential requirements of cognitive radios: spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility. In this work, spectrum sensing based on energy detection was investigated. An algorithm was developed for the determination of channel occupation and the number of samples needed for the detection process to achieve pre-established probabilities of detection and false-alarm. Simulations results are presented showing that noise uncertainty degrade the performance of the system when the signal-to-noise ratio is low. The developed algorithm allows determining a lower threshold for the signal-to-noise ratio, when noise uncertainty exists. The detection probability behavior as a function of the false alarm probability having the number of samples and the signal-to noise ratio as parameters is presented. The resulting curves are often denominated ROC - Receiver Operation Characteristics in the literature. Due to the high social and political interest in the TV broadcasting band, that telecommunications scenario currently presents, this band was chosen for same examples in this study.