Cooperative Wideband Spectrum Sensing Based on Joint Sparsity

COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON JOINT SPARSITY By Ghazaleh Jowkar, Master of Science A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University Virginia Commonwealth University 2017 Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering In this thesis, the problem of wideband spectrum sensing in cognitive radio (CR) networks using sub-Nyquist sampling and sparse signal processing techniques is investigated. To mitigate multi-path fading, it is assumed that a group of spatially dispersed SUs collaborate for wideband spectrum sensing, to determine whether or not a channel is occupied by a primary user (PU). Due to the underutilization of the spectrum by the PUs, the spectrum matrix has only a small number of non-zero rows. In existing state-of-the-art approaches, the spectrum sensing problem was solved using the low-rank matrix completion technique involving matrix nuclear-norm minimization. Motivated by the fact that the spectrum matrix is not only low-rank, but also sparse, a spectrum sensing approach is proposed based on minimizing a mixed-norm of the spectrum matrix instead of low-rank matrix completion to promote the joint sparsity among the column vectors of the spectrum matrix. Simulation results are obtained, which demonstrate that the proposed mixed-norm minimization approach outperforms the low-rank matrix completion based approach, in terms of the PU detection performance. Further we used mixed-norm minimization model in multi time frame detection. Simulation results shows that increasing the number of time frames will increase the detection performance, however, by increasing the number of time frames after a number of times the performance decrease dramatically

Spectrum sensing algorithms and software-defined radio implementation for cognitive radio system

The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. However, measurements showed that a large part of frequency channels are underutilized or almost unoccupied. The cognitive radio paradigm arises as a tempting solution to the spectral congestion problem. A cognitive radio must be able to identify transmission opportunities in unused channels and to avoid generating harmful interference with the licensed primary users. Its key enabling technology is the spectrum sensing unit, whose ultimate goal consists in providing an indication whether a primary transmission is taking place in the observed channel. Such indication is determined as the result of a binary hypothesis testing experiment wherein null hypothesis (alternate hypothesis) corresponds to the absence (presence) of the primary signal. The first parts of this thesis describes the spectrum sensing problem and presents some of the best performing detection techniques. Energy Detection and multi-antenna Eigenvalue-Based Detection algorithms are considered. Important aspects are taken into account, like the impact of noise estimation or the effect of primary user traffic. The performance of each detector is assessed in terms of false alarm probability and detection probability. In most experimental research, cognitive radio techniques are deployed in software-defined radio systems, radio transceivers that allow operating parameters (like modulation type, bandwidth, output power, etc.) to be set or altered by software.In the second part of the thesis, we introduce the software-defined radio concept. Then, we focus on the implementation of Energy Detection and Eigenvalue-Based Detection algorithms: first, the used software platform, GNU Radio, is described, secondly, the implementation of a parallel energy detector and a multi-antenna eigenbased detector is illustrated and details on the used methodologies are given. Finally, we present the deployed experimental cognitive testbeds and the used radio peripherals. The obtained algorithmic results along with the software-defined radio implementation may offer a set of tools able to create a realistic cognitive radio system with real-time spectrum sensing capabilities

Wideband Spectrum Sensing for Dynamic Spectrum Sharing

The proliferation of wireless devices grows exponentially, demanding more and more data communication capacity over wireless links. Radio spectrum is a scarce resource, and traditional wireless networks deployed by Mobile Network Operators (MNO) are based on an exclusive spectrum band allocation. However, underutilization of some licensed bands in time and geographic domains has been reported, especially in rural areas or areas away from high population density zones. This coexistence of increasingly high data communication needs and spectrum underutilization is an incomprehensible scenario. A more rational and efficient use of the spectrum is the possibility of Licensed Users (known as Primary Users – PU) to lease the spectrum, when not in use, to Unlicensed Users (known as Secondary Users – SU), or allowing the SU to opportunistically use the spectrum after sensing and verifying that the PU is idle. In this latter case, the SU must stop transmitting when the PU becomes active. This thesis addresses the spectrum sensing task, which is essential to provide dynamic spectrum sharing between PUs and SUs. We show that the Spectral Correlation Function (SCF) and the Spectral Coherence Function (SCoF) can provide a robust signal detection algorithm by exploiting the cyclostationary characteristics of the data communication signal. We enhance the most used algorithm to compute de SCF - the FAM (FFT Accumulation Method) algorithm – to efficiently compute the SCF in a local/zoomed region of the support ( ; ) plane (frequency/cycle frequency plane). This will provide the quick identification of spectral bands in use by PUs or free, in a wideband sampling scenario. Further, the characterization of the probability density of the estimates of the SCF and SCoF when only noise is present, using the FAM algorithm, will allow the definition of an adaptive threshold to develop a blind (with respect to the noise statistics) Constant False Alarm Rate (CFAR) detector (using the SCoF) and also a CFAR and a Constant Detection Rate (CDR) detector when that characterization is used to obtain an estimate of the background noise variance (using the SCF).A proliferação de dispositivos sem fios cresce de forma exponencial, exigindo cada vez mais capacidade de comunicação de dados através de ligações sem fios. O espectro radioelétrico é um recurso escasso, e as redes sem fios tradicionais implantadas pelos Operadores de Redes Móveis baseiam-se numa atribuição exclusiva de bandas do espectro. No entanto, tem sido relatada a subutilização de algumas bandas licenciadas quer ao longo do tempo, quer na sua localização geográfica, especialmente em áreas rurais, e em áreas longe de zonas de elevada densidade populacional. A coexistência da necessidade cada vez maior de comunicação de dados, e a subutilização do espectro é um cenário incompreensível. Uma utilização mais racional e eficiente do espectro pressupõe a possibilidade dos Utilizadores Licenciados (conhecidos como Utilizadores Primários – Primary Users - PU) alugarem o espectro, quando este não está a ser utilizado, a Utilizadores Não Licenciados (conhecidos como Utilizadores Secundários – Secondary Users - SU), ou permitir ao SU utilizar oportunisticamente o espectro após a deteção e verificação de que o PU está inativo. Neste último caso, o SU deverá parar de transmitir quando o PU ficar ativo. Nesta tese é abordada a tarefa de deteção espectral, que é essencial para proporcionar a partilha dinâmica do espectro entre PUs e SUs. Mostra-se que a Função de Correlação Espectral (Spectral Correlation Function - SCF) e a Função de Coerência Espectral (Spectral Coherence Function - SCoF) permitem o desenvolvimento de um algoritmo robusto de deteção de sinal, explorando as características ciclo-estacionárias dos sinais de comunicação de dados. Propõe-se uma melhoria ao algoritmo mais utilizado para cálculo da SCF – o método FAM (FFT Accumulation Method) - para permitir o cálculo mais eficiente da SCF numa região local/ampliada do plano de suporte / (plano de frequência/frequência de ciclo). Esta melhoria permite a identificação rápida de bandas espectrais em uso por PUs ou livres, num cenário de amostragem de banda larga. Adicionalmente, é feita a caracterização da densidade de probabilidade das estimativas da SCF e SCoF quando apenas o ruído está presente, o que permite a definição de um limiar adaptativo, para desenvolver um detetor de Taxa de Falso Alarme Constante (Constant False Alarm Rate – CFAR) sem conhecimento do ruído de fundo (usando a SCoF) e também um detetor CFAR e Taxa de Deteção Constante (Constant Detection Rate – CDR), quando se utiliza aquela caracterização para obter uma estimativa da variância do ruído de fundo (usando a SCF)

Combined Soft Hard Cooperative Spectrum Sensing in Cognitive Radio Networks

Providing some techniques to enhance the performance of spectrum sensing in cognitive radio systems while accounting for the cost and bandwidth limitations in practical scenarios is the main objective of this thesis. We focus on an essential element of cooperative spectrum sensing (CSS) which is the data fusion that combines the sensing results to make the final decision. Exploiting the advantage of the superior performance of the soft schemes and the low bandwidth of the hard schemes by incorporating them in cluster based CSS networks is achieved in two different ways. First, a soft-hard combination is employed to propose a hierarchical cluster based spectrum sensing algorithm. The proposed algorithm maximizes the detection performances while satisfying the probability of false alarm constraint. Simulation results of the proposed algorithm are presented and compared with existing algorithms over the Nakagami fading channel. Moreover, the results show that the proposed algorithm outperforms the existing algorithms. In the second part, a low complexity soft-hard combination scheme is suggested by utilizing both one-bit and two-bit schemes to balance between the required bandwidth and the detection performance by taking into account that different clusters undergo different conditions. The scheme allocates a reliability factor proportional to the detection rate to each cluster to combine the results at the Fusion center (FC) by extracting the results of the reliable clusters. Numerical results obtained have shown that a superior detection performance and a minimum overhead can be achieved simultaneously by combining one bit and two schemes at the intra-cluster level while assigning a reliability factor at the inter-cluster level