The cognitive radio-based wireless networks have been proposed as a promising technology to improve the utilization of the radio spectrum through opportunistic spectrum access. In this context, the cognitive radios opportunistically access the spectrum which is licensed to primary users when the primary user transmission is detected to be absent. For opportunistic spectrum access, the cognitive radios should sense the radio environment and allocate the spectrum and power based on the sensing results. To this end, in this thesis, I develop a novel cooperative spectrum sensing scheme for cognitive radio networks (CRNs) based on machine learning techniques which are used for pattern classification. In this regard, unsupervised and supervised learning-based classification techniques are implemented for cooperative spectrum sensing. Secondly, I propose a novel joint channel and power allocation scheme for downlink transmission in cellular CRNs. I formulate the downlink resource allocation problem as a generalized spectral-footprint minimization problem. The channel assignment problem for secondary users is solved by applying a modified Hungarian algorithm while the power allocation subproblem is solved by using Lagrangian technique. Specifically, I propose a low-complexity modified Hungarian algorithm for subchannel allocation which exploits the local information in the cost matrix. Finally, I propose a novel dynamic common control channel-based medium access control (MAC) protocol for CRNs. Specifically, unlike the traditional dedicated control channel-based MAC protocols, the proposed MAC protocol eliminates the requirement of a dedicated channel for control information exchange. / October 2015

Les systèmes de communications sans fil ne cessent de se multiplier pour devenir incontournables de nos jours. Cette croissance cause une augmentation de la demande des ressources spectrales, qui sont devenues de plus en plus rares. Afin de résoudre ce problème de pénurie de fréquences, Joseph Mitola III, en 2000, a introduit l'idée de l'allocation dynamique du spectre. Il définit ainsi le terme " Cognitive Radio " (Radio Intelligente), qui est largement pressenti pour être le prochain Big Bang dans les futures communications sans fil [1]. Dans le cadre de ce travail on s'intéresse à la problématique du spectrum sensing qui est la détection de présence des Utilisateurs Primaires dans un spectre sous licence, dans le contexte de la radio intelligente. L'objectif de ce travail est de proposer des méthodes de détection efficaces à faible complexité et/ou à faible temps d'observation et ceci en utilisant le minimum d'information a priori sur le signal à détecter. Dans la première partie on traite le problème de détection d'un signal aléatoire dans le bruit. Deux grandes méthodes de détection sont utilisées : la détection d'énergie ou radiomètre et la détection cyclostationnaire. Dans notre contexte, ces méthodes sont plus complémentaires que concurrentes. Nous proposons une architecture hybride de détection des bandes libres, qui combine la simplicité du radiomètre et la robustesse des détecteurs cyclostationnaires. Deux méthodes de détection sont proposées qui se basent sur cette même architecture. Grâce au caractère adaptatif de l'architecture, la détection évolue au cours du temps pour tendre vers la complexité du détecteur d'énergie avec des performances proches du détecteur cyclostationnaire ou du radiomètre selon la méthode utilisée et l'environnement de travail. Dans un second temps on exploite la propriété parcimonieuse de la Fonction d'Autocorrelation Cyclique (FAC) pour proposer un nouvel estimateur aveugle qui se base sur le compressed sensing afin d'estimer le Vecteur d'Autocorrelation Cyclique (VAC), qui est un vecteur particulier de la Fonction d'Autocorrelation Cyclique pour un délai fixe. On montre par simulation que ce nouvel estimateur donne de meilleures performances que celles obtenues avec l'estimateur classique, qui est non aveugle et ceci dans les mêmes conditions et en utilisant le même nombre d'échantillons. On utilise l'estimateur proposé, pour proposer deux détecteurs aveugles utilisant moins d'échantillons que nécessite le détecteur temporel de second ordre de [2] qui se base sur l'estimateur classique de la FAC. Le premier détecteur exploite uniquement la propriété de parcimonie du VAC tandis que le second détecteur exploite en plus de la parcimonie la propriété de symétrie du VAC, lui permettant ainsi d'obtenir de meilleures performances. Ces deux détecteurs outre qu'ils sont aveugles sont plus performants que le détecteur non aveugle de [2] dans le cas d'un faible nombre d'échantillons.

Made available in DSpace on 2014-12-17T14:56:19Z (GMT). No. of bitstreams: 1 ArthurDLL_DISSERT.pdf: 2517302 bytes, checksum: c3d693c770dc1c58bad5f378aba6d268 (MD5) Previous issue date: 2014-06-28 / The increasing demand for high performance wireless communication systems has shown the inefficiency of the current model of fixed allocation of the radio spectrum. In this context, cognitive radio appears as a more efficient alternative, by providing opportunistic spectrum access, with the maximum bandwidth possible. To ensure these requirements, it is necessary that the transmitter identify opportunities for transmission and the receiver recognizes the parameters defined for the communication signal. The techniques that use cyclostationary analysis can be applied to problems in either spectrum sensing and modulation classification, even in low signal-to-noise ratio (SNR) environments. However, despite the robustness, one of the main disadvantages of cyclostationarity is the high computational cost for calculating its functions. This work proposes efficient architectures for obtaining cyclostationary features to be employed in either spectrum sensing and automatic modulation classification (AMC). In the context of spectrum sensing, a parallelized algorithm for extracting cyclostationary features of communication signals is presented. The performance of this features extractor parallelization is evaluated by speedup and parallel eficiency metrics. The architecture for spectrum sensing is analyzed for several configuration of false alarm probability, SNR levels and observation time for BPSK and QPSK modulations. In the context of AMC, the reduced alpha-profile is proposed as as a cyclostationary signature calculated for a reduced cyclic frequencies set. This signature is validated by a modulation classification architecture based on pattern matching. The architecture for AMC is investigated for correct classification rates of AM, BPSK, QPSK, MSK and FSK modulations, considering several scenarios of observation length and SNR levels. The numerical results of performance obtained in this work show the eficiency of the proposed architectures / O aumento da demanda por sistemas de comunica??o sem fio de alto desempenho tem evidenciado a inefici?ncia do atual modelo de aloca??o fixa do espectro de r?dio. Nesse contexto, o r?dio cognitivo surge como uma alternativa mais eficiente, ao proporcionar o acesso oportunista ao espectro, com a maior largura de banda poss?vel. Para garantir esses requisitos, ? necess?rio que o transmissor identifique as oportunidades de transmiss?o e que o receptor reconhe?a os par?metros definidos para o sinal de comunica??o. As t?cnicas que utilizam a an?lise cicloestacion?ria podem ser aplicadas tanto em problemas de sensoriamento espectral, quanto na classifica??o de modula??es, mesmo em ambientes de baixa rela??o sinal-ru?do (SNR). Entretanto, apesar da robustez, uma das principais desvantagens da cicloestacionariedade est? no elevado custo computacional para o c?lculo das suas fun??es. Este trabalho prop?e arquiteturas eficientes de obten??o de caracter?sticas cicloestacion?rias para serem empregadas no sensoriamento espectral e na classifica??o autom?tica de modula??es (AMC). No contexto do sensoriamento espectral, um algoritmo paralelizado para extrair as caracter?sticas cicloestacion?rias de sinais de comunica??o ? apresentado. O desempenho da paraleliza??o desse extrator de caracter?sticas ? avaliado atrav?s das m?tricas de speedup e efici?ncia paralela. A arquitetura de sensoriamento espectral ? analisada para diversas configura??es de probabilidades de falso alarme, n?veis de SNR e tempo de observa??o das modula??es BPSK e QPSK. No contexto da AMC, o perfil-alfa reduzido ? proposto como uma assinatura cicloestacion?ria calculada para um conjunto reduzido de frequ?ncia c?clicas. Essa assinatura ? validada por meio de uma arquitetura de classifica??o baseada no casamento de padr?es. A arquitetura para AMC ? investigada para as taxas de acerto obtidas para as modula??es AM, BPSK, QPSK, MSK e FSK, considerando diversos cen?rios de tempo de observa??o e n?veis de SNR. Os resultados num?ricos de desempenho obtidos neste trabalho demonstram a efici?ncia das arquiteturas propostas

Cognitive radio technology has been an important area of research in telecommunications for solving the problem of spectrum scarcity. That\'s because in addition to allowing dynamic allocation of the electromagnetic spectrum, cognitive radios must be able to identify the non cognitive user\'s transmission on the channel. This operation is only possible through the continuous sensing of the electromagnetic spectrum. In this context, this paper presents a detailed study on spectrum sensing, an important stage in cognitive radio technology. For the presentation of this work, a detailed study on software dened radio (SDR) was carried out, without which it would be impossible to work with cognitive radios, once they are implemented by means of SDR technology. It was also presented the tools GNU Radio and USRP, which together form a solution of SDR, through implementation of AM receivers. The theoretical foundations of spectrum sensing and blind source separation (BSS) are presented and then is made a detailed study of the use of BSS for spectral sensing. From the study of BSS, it is possible to use new metrics for decision making about the presence or the absence of a primary user in the channel. Throughout the study, simulations and implementations were conducted on MATLAB in order to perform various situations, and, nally, it is presented outcomes and conclusions reached during the work. / A tecnologia de rádio cognitivo tem sido uma importante área de pesquisa em telecomunicações para a solução do problema da escassez espectral. Isto porque, além de permitirem a alocação dinâmica do espectro eletromagnético, os rádios cognitivos devem ser capazes de identificar as transmissões de usuários não cognitivos no canal. Esta operação só é possível por meio do sensoriamento contínuo do espectro eletromagnético. Neste contexto, este trabalho apresenta um estudo detalhado sobre o sensoriamento de espectro, uma importante etapa da tecnologia de rádios cognitivos. Para a apresentação deste trabalho foi realizado um estudo detalhado a respeito de rádio definido por software (SDR), sem o qual não seria possível o trabalho com rádios cognitivos, uma vez que este é implementado por meio da tecnologia de SDR. Também foram apresentadas as ferramentas GNU Radio e USRP, que, juntas, formam uma solução de SDR, por meio de implementações de receptores AM. Os fundamentos teóricos de sensoriamento de espectro e separação cega de fontes (BSS) são apresentados e, em seguida, é realizado um estudo aprofundado do uso de BSS para o sensoriamento espectral. A partir do estudo de BSS, é possível utilizar novas métricas de decisão a respeito da presença ou não de um usuário primário no canal. Durante todo este trabalho foram realizadas implementações e simulações no MATLAB com a finalidade de executar diversas situações e, finalmente, são apresentados resultados verificados e conclusões obtidas neste trabalho. / Mestre em Ciências

Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-02-16T09:52:46Z No. of bitstreams: 1 alamsilvamenezes.pdf: 8301590 bytes, checksum: aed618e30f38206da4bf4f329924f87e (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-02-26T12:30:53Z (GMT) No. of bitstreams: 1 alamsilvamenezes.pdf: 8301590 bytes, checksum: aed618e30f38206da4bf4f329924f87e (MD5) / Made available in DSpace on 2016-02-26T12:30:53Z (GMT). No. of bitstreams: 1 alamsilvamenezes.pdf: 8301590 bytes, checksum: aed618e30f38206da4bf4f329924f87e (MD5) Previous issue date: 2014-09-01 / A presente tese propõe soluções ao problema da explicitação do conteúdo espectral de processos estacionários e não estacionários, com aplicações na estimação de frequência, estimação da densidade espectral de potência e no monitoramento do espectro. A técnica de estimação de frequência proposta nesta tese, baseada na warped discrete Fourier transform, apresenta, de acordo com as simulações computacionais, o melhor desempenho frente às demais técnicas comparadas, atingindo o Cramer-Rao bound para uma ampla faixa de relação sinal ruído. Em relação a estimação da densidade espectral de potência, a Hartley Multitaper method, proposta nesta tese, apresenta desempenho similar à multitaper method, em termos da variância de estimação e da polarização do espectro, mas simpli cação de implementação. Uma técnica para monitoramento do espectro para sistemas power line communication é proposta, levando em consideração o conceito de quanta e a diversidade observada quando os sinais são aquisitados a partir da rede de energia elétrica e do ar. Baseando-se em sinais sintéticos, gerados em computador, assim como dados de medição do espectro, obtidos utilizando uma antena e o cabo de energia elétrica como elementos sensores, veri fica-se que o desempenho da técnica proposta supera a monitoração padrão, sobretudo quando a diversidade gerada pelo cabo e pela antena sobre o sinal monitorado é explorada na detecção. / This dissertation aims at discussing solutions to deal with spectral analysis of stationary and non-stationary processes for frequency estimation, power spectral density estimation and spectral monitoring applications. The frequency estimation techniques are assessed through computer simulations. The proposed technique for frequency estimation is based on warped discrete Fourier transform outperforms other techniques, achieving the Cramer-Rao Bound for a wide range of signal to noise ratio. Regarding the power spectral density estimation, the proposed Hartley Multitaper Method shows similar performance, in terms of variance of estimates and polarization spectrum; however, it can simplify the implementation complexity. The introduced spectrum sensing technique is based on quanta de nition and the diversity o ered by the signals acquired from the electric power grids and the air. Based on computer-generation data and those one obtained during a measurement campaign, which one in this thesis is evaluated using synthetic signals, generated by computer, as well as measurement data of the spectrum. The numerical results show that the proposed technique outperforms a previous technique and can attain the very detection ratio and the very low false alarm when the diversity yielded by electric power grid and air is exploited.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-04-20T18:23:07Z No. of bitstreams: 1 laryssaramosamado.pdf: 2344885 bytes, checksum: 4328135ddbd0305fc11aa0bf0f8f8b61 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-04-24T16:50:29Z (GMT) No. of bitstreams: 1 laryssaramosamado.pdf: 2344885 bytes, checksum: 4328135ddbd0305fc11aa0bf0f8f8b61 (MD5) / Made available in DSpace on 2017-04-24T16:50:29Z (GMT). No. of bitstreams: 1 laryssaramosamado.pdf: 2344885 bytes, checksum: 4328135ddbd0305fc11aa0bf0f8f8b61 (MD5) Previous issue date: 2011-08-29 / CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico / A presente dissertação tem como objetivos principais a discussão e a análise do uso de técnicas de monitoramento de espectro aplicadas a sistemas PLC, para que a ocupação deste espectro seja explicitada. Neste contexto, diversas técnicas de processamento de sinais e inteligência computacional são utilizadas para extrair e selecionar o menor número de características que sejam mais representativas para detecção, a fim de projetar o melhor e menos complexo detector de sinais a ser utilizado inicialmente na faixa de frequência entre 1,705 e 100 MHz, mas que permita futuras modificações para aplicações na faixa entre 1,705 e 250 MHz. Além disso, o problema de monitoramento de espectro para sistemas PLC é formalizado, e questões de investigação são analisadas tanto para dados simulados em MATLAB quanto para dados medidos em campo. O processo de medição destes dados é descrito e suas características são explicitadas. Finalmente, a análise dos resultados obtidos indica a adequabilidade das técnicas aplicadas ao problema em questão, porém indicam necessidade do aprofundamento desta investigação. Desta maneira, este trabalho consiste em um estudo inicial sobre importantes questões pertinentes ao monitoramento de espectro de sistemas PLC. / This master thesis aims to discuss and analyze the use of spectrum sensing techniques applied to PLC systems, in order to explicit the spectrum occupation. These techniques extract and select the least quantity of the most representative signal features in order to project the best detector that presents the lowest computational complexity. In addition to that, the spectrum sensing problem is formalized, and a few investigation questions are analyzed for both synthetic and measured data. The measurement of PLC signals and their characterization is also exposed. Although the analysis of the attained results indicate that the techniques used are suitable for the examined problems, their further investigation is necessary, in order to better understand the PLC environment and the spectrum sensing issues related to it. This work is, therefore, an initial study about the mentioned matters.

Abstract Cognitive radio (CR) is becoming a promising tool for solving the problem of the scarce radio resource and spectrum inefficiency. Spectrum sensing (signal detection) enables real-time detection of spectrum holes by unlicensed secondary users (SUs) in cognitive radio networks (CRNs). In this thesis, performance analysis of CRNs and radio resource allocation are considered. A continuous time Markov chain (CTMC) based analytical model taking into account all relevant elements as well as addressing the issue of the false alarm rate (FAR) associated with the continuous sensing is developed. In some cases, the PU can be modeled as time-slotted with constant state (transmitting or not) in each slot. In this case, assuming SU can synchronize to the slots, its intuitive to use beginning of a slot for sensing and rest (possibly) for communication. For this model, M/D/1 priority queueing scheme has been applied in this thesis to find waiting time and queue length for PU and SU. Multiple access among SUs in a time-slotted channel is considered next. A conventional method is e.g. using a channel access probability ψ in each slot similar to the slotted ALOHA. A radically new idea is introduced in this thesis: why not increase the false alarm probability PFA of each SU and use it as a multiple access method? A game theoretic approach to radio resource allocation for the downlink capacity providing fair resource sharing among mobile nodes located along a multihop link is presented. Furthermore, the problem of resource allocations in heterogeneous wireless networks is also studied. Finally, device-to-device (D2D) communication - with localized distribution, where users tend to gather around some areas (clusters/hot-spots) within the cell such as buildings is studied. Theoretical analysis with two dimensional clustering is presented including cases with correlated clusters. Correlation in cluster selection is shown to significantly improve performance. / Tiivistelmä Kognitiivinen radio (CR) on nousemassa lupaavaksi työkaluksi niukkojen radioresurssien ja spektrin käytön tehottomuuden ratkaisemisessa. Spektrin nuuskiminen (signaalin ilmaisu) mahdollistaa spektriaukkojen reaaliaikaisen tunnistamisen toissijaisten käyttäjien (SU) toimesta kognitiivisissa radioverkoissa (CRN). Tässä väitöskirjassa painotus on CRN verkkojen suorituskykyanalyysissa ja radioresurssien hallinnassa. Työssä kehitetään jatkuva-aikaiseen Markov ketjuun (CTMC) perustuva analyyttinen malli joka ottaa huomioon kaikki olennaiset asiat mukaan lukien jatkuva-aikaiseen spektrin nuuskimiseen liittyvän väärien hälytysten tiheyden (FAR). Joissakin tapauksissa PU:ta voidaan mallintaa aikajaoteltuna siten että PU:n tila on vakio kussakin aikavälissä. Olettaen että SU voi synkronoitua aikaväleihin, on intuitiivista käyttää aikavälin alkua nuuskimiselle ja loppuosaa (mahdollisesti) viestintää varten. M/D/1:n ensisijaisuus-jonotus-suunnitelmaa soveltamalla tässä väitöskirjassa saadaan tuloksia odotusajalle ja jonon pituudelle sekä SU:lle että PU:lle. Seuraavaksi käsitellään monikäyttöä SU:den joukossa aikajaotellussa kanavassa. Tavanomainen menetelmä käyttää esimerkiksi kanavapääsytodennäköisyyttä ψ kussakin aikavälissä vastaten aikajaoteltua ALOHA protokollaa. Tässä väitöskirjassa esitetään radikaali uusi idea: miksei lisätä väärän hälytyksen todennäköisyyttä kussakin SU:ssa ja käytetä sitä moniliittymämenetelmänä? Työssä esitetään peliteoreettinen lähestymistapa radioresurssien allokointiin siten että resurssit jaetaan oikeudenmukaisesti monen yhteysvälin linkeissä. Lisäksi tutkitaan myös resursoinnin ongelmaa heterogeenisissa langattomissa verkoissa. Lopuksi tutkitaan laitteiden välistä suoraa viestintää (D2D) paikallisen jakauman kanssa, jossa käyttäjillä on tapana kasaantua solun sisällä esim. rakennuksiin. Esitetään teoreettinen analyysi kaksiulotteisella klusteroinnilla myös korreloitujen ryhmien kanssa. Osoitetaan että korrelaatio ryhmän valinnassa parantavaa merkittävästi suorituskykyä.

This thesis proposes and analyzes data fusion algorithms that operate on the physical layer of a wireless sensor network, in the context of three applications of cognitive radios: 1. Cooperative spectrum sensing via binary consensus; 2. Multiple transmitter localization and communication footprint identification; 3.Target self-localization using beacon nodes. For the first application, a co-phasing based data combining scheme is studied under imperfect channel knowledge. The evolution of network consensus state is modeled as a Markov chain, and the average transition probability matrix is derived. Using this, the average hitting time and average consensus duration are obtained, which are used to determine and optimize the performance of the consensus procedure. Second, using the fact that a typical communication footprint map admits a sparse representation, two novel compressed sensing based schemes are proposed to construct the map using 1-bit decisions from sensors deployed in a geographical area. The number of transmitters is determined using the K-means algorithm and a circular fitting technique, and a design procedure is proposed to determine the power thresholds for signal detection at sensors. Third, an algorithm is proposed for self-localization of a target node using power measurements from beacon nodes transmitting from known locations. The geographical area is overlaid with a virtual grid, and the problem is treated as one of testing overlapping subsets of grid cells for the presence of the target node. The column matching algorithm from group testing literature is considered for devising the target localization algorithm. The average probability of localizing the target within a grid cell is derived using the tools from Poisson point processes and order statistics. This quantity is used to determine the minimum required node density to localize the target within a grid cell with high probability. The performance of all the proposed algorithms is illustrated through Monte Carlo simulations.

Mechanical vibrations in large structures such as buildings, bridges, dams and critical frequencies in large machinery generally have low frequencies (100Hz-1000Hz). To monitor large areas of such structures we need huge network of low cost, easily manufacturable, self-powered and stand-alone vibration spectrum sensors. The sensors should also consume very little power during their overall operation cycle and have moderately high frequency resoultion. The thesis provides mathematical analysis, design and development of stand-alone, low frequency vibration spectrum analyzer .A mechanically stretched polymer piezoelectric membrane, which has a fixed length and tension, can act as a single frequency detector due to its unique resonant frequency. Stretching multiple ribbons of diffferent lengths and tensions, a vibration spectrum analyzer, which gives the Fourier frequency components present in an arbitrary mechanical input vibration, can be designed. The thesis presents a detailed description of experiments to evaluate a low frequency vibration spectrum analyzer system that accepts an incoming input vibration and directly provides the spectrum as output. Polymer piezoelectric materials being easily manufacturable these sensors can be deployed in wide area sensor networks that monitor large structures. The thesis also shows design of a vibration energy harvesting system based on the concept of harvesting energy at low frequencies. The need for developing such an energy harvesting system arises from the necessity of making the vibration sensor, self-powered. Multiple experimental tests were performed before developing a prototype vibration energy harvesting circuit.

The doctoral thesis deals with spectrum sensing in cognitive radio networks (CRN). A number of international organizations are currently actively engaged in standardization of CRN and it points out to the fact that this technology will be widely used in the near future. One of the key features of this technology is a dynamic access to the spectrum, which can be affected by many different harmful factors occurring in the communication chain. The thesis investigates the influence of selected factors on the spectrum sensing process. Another contribution of the work is the optimization of the Kolmogorov - Smirnov statistical test that can be applied for the primary user signal detection. The work also incorporates the analysis of the influence of the harmful effects caused by the commonly used transmitters and receivers on various spectrum sensing methods. The investigations are verified by the results of the simulations and also by the measurements with experimental platforms based on the software-defined radio (SDR).