Institut d'Électronique et de Télécommunications de Rennes
UMR CNRS 6164

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Soutenance de thèse

Hussein KOBEISSI soutient sa thèse intitulée :

Détecteurs de bandes libres utilisant les valeurs propres pour la radio intelligente multi-antennes : comportement asymptotique et non-asymptotique

La soutenance aura lieu à l’Université Libanaise de Beyrouth et sera retransmise sur le campus de Rennes, le mardi 13 décembre 2016 à 14h00 en salle immersive à CentraleSupélec.

Abstract : Eigenvalue Based Detector in Finite and Asymptotic Multi-antenna Cognitive Radio Systems

During the last decades, wireless communications have visualized an exponential growth due to rapidly expanding market of wireless broadband and multimedia users and applications.

Indeed, the demand for more radio spectrum increased in order to support this growth which highlighted on the scarcity and under-utilization problems of the radio spectrum resources.

To this end, Cognitive Radio (CR) technology has received an enormous attention as an emerging solution to the spectrum shortage problem for the next generation wireless communication systems.

For the CR to operate efficiently and to provide the required improvement in spectrum efficiency, it must be able to effectively identify the spectrum holes. Thus, Spectrum Sensing (SS) is the key element and critical component of the CR technology. In CR networks, Spectrum Sensing (SS) is the task of obtaining awareness about the spectrum usage. Mainly it concerns two scenarios of detection : (i) detecting the absence of the Primary User (PU) in a licensed spectrum in order to use it and (ii) detecting the presence of the PU to avoid interference. Several SS techniques were proposed in the literature.

Among these, Eigenvalue Based Detector (EBD) has been proposed as a precious totally-blind detector that exploits the spacial diversity, overcome noise uncertainty challenges and performs adequately even in low SNR conditions. However, the complexity of the distributions of decision metrics of the EBD is one of the important challenges. Moreover, the use massive MIMO technology in SS is still not explored.

The first part of this study concerns the Standard Condition Number (SCN) detector and the Scaled Largest Eigenvalue (SLE) detector. The focus is on the complexity of the statistical distributions of the SCN and the SLE decision metrics since this will imply a complicated expressions for the performance probabilities as well as the decision threshold if it could be derived. We derive exact expressions for the Probability Density Function (PDF) and the Cumulative Distribution Function (CDF) of the SCN using results from finite Random Matrix Theory (RMT). In addition, we derived exact expressions for the moments of the SCN and we proposed a new approximation based on the Generalized Extreme Value (GEV) distribution. Moreover, using results from the asymptotic RMT we further provide a simple forms for the central moments of the SCN and we end up with a simple and accurate expression for the CDF, PDF, Probability of False-Alarm (Pfa), Probability of Detection (Pd), Probability of Miss-Detection (Pmd) and the decision threshold that could be computed on the y and hence provide a dynamic SCN detector that could dynamically change the threshold value depending on target performance and environmental conditions. On the other hand, we proved that the SLE decision metric could be modelled using Gaussian function and hence we derived its PDF, CDF, Pfa, Pd and decision threshold.

In addition, we also considered the correlation between the largest eigenvalue and the trace in the SLE study. The second part of this study concerns the massive MIMO technology and how to exploit the large number of antennas for SS and CRs. Two antenna exploitation scenarios are studied : (i) Full antenna exploitation and (ii) Partial antenna exploitation in which we have two options : (i) Fixed use or (ii) Dynamic use of the antennas. We considered the Largest Eigenvalue (LE) detector if noise power is perfectly known and the SCN and SLE detectors when noise uncertainty exists. For fixed approach, we derived the optimal threshold which minimizes the error probabilities. For the dynamic approach, we derived the equation from which one can compute the minimum requirements of the system. For full exploitation, asymptotic approximation of the threshold is considered using the GEV distribution. Finally, a comparisons between these scenarios and different detectors are provided in terms of system performance and minimum requirements. This work presents a novel study in the field of SS applications in CR with massive MIMO technology.






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