Compressive Sensing Over TV White Space in Wideband Cognitive Radio

PhDSpectrum scarcity is an important challenge faced by high-speed wireless communications. Meanwhile, caused by current spectrum assignment policy, a large portion of spectrum is underutilized. Motivated by this, cognitive radio (CR) has emerged as one of the most promising candidate solutions to improve spectrum utilization, by allowing secondary users (SUs) to opportunistically access the temporarily unused spectrum, without introducing harmful interference to primary users. Moreover, opening of TV white space (TVWS) gives us the con dence to enable CR for TVWS spectrum. A crucial requirement in CR networks (CRNs) is wideband spectrum sensing, in which SUs should detect spectral opportunities across a wide frequency range. However, wideband spectrum sensing could lead to una ordably high sampling rates at energy-constrained SUs. Compressive sensing (CS) was developed to overcome this issue, which enables sub-Nyquist sampling by exploiting sparse property. As the spectrum utilization is low, spectral signals exhibit a natural sparsity in frequency domain, which motivates the promising application of CS in wideband CRNs. This thesis proposes several e ective algorithms for invoking CS in wideband CRNs. Speci cally, a robust compressive spectrum sensing algorithm is proposed for reducing computational complexity of signal recovery. Additionally, a low-complexity algorithm is designed, in which original signals are recovered with fewer measurements, as geolocation database is invoked to provide prior information. Moreover, security enhancement issue of CRNs is addressed by proposing a malicious user detection algorithm, in which data corrupted by malicious users are removed during the process of matrix completion (MC). One key spotlight feature of this thesis is that both real-world signals and simulated signals over TVWS are invoked for evaluating network performance. Besides invoking CS and MC to reduce energy consumption, each SU is supposed to harvest energy from radio frequency. The proposed algorithm is capable of o ering higher throughput by performing signal recovery at a remote fusion center

Monitorización del espectro multibanda en radios cognoscitivos

En este trabajo de investigación se plantean tres propuestas novedosas para la monitorización del espectro multibanda en un contexto de radios cognoscitivos. Estas metodologías hacen uso de herramientas específicas para la detección de los bordes de huecos disponibles en el espectro de banda ancha como: el modulo máximo de la transformada continua de wavelet, análisis multiresolución y algunos algoritmos de machine learning (red neuronal, expectation maximization, k-means y Dirichlet process gaussian mixture model). Además el análisis multiresolución se combina con la dimensión fractal de Higuchi (una medida no lineal) para establecer la regla de decisión que permite determinar la presencia o ausencia de un usuario primario en el espectro de banda ancha analizado. Cada una de estas propuestas se probó en un entorno controlado (simulación) teniendo buenos resultados para una relación señal a ruido mayor a 0 dB de 95 %, 98 % y 99 % para la 1ª 2ª y 3ª metodología, respectivamente. Además estas propuestas se probaron en señales recuperadas del entorno (señales reales). Con base en lo anterior estos métodos propuestos son opciones efectivas para detectar la actividad del usuario primario en el espectro multibanda

Nonconvex Optimization of Collaborative Multiband Spectrum Sensing for Cognitive Radios with Genetic Algorithms

Cognitive Radio (CR) is a novel technology that permits secondary users (SUs) to transmit alongside primary users (PUs). PUs retain transparent communications whereas SUs perform spectrum sensing and adaptive transmission to avoid collisions. Ultra-wideband sensing is of primary importance for SU to sense and access opportunistically several bands at a time. Reliable detection in wide geographical regions needs collaborative sensing. Optimal collaborative multiband sensing is not analytically solvable unless some approximations and solution domain restrictions are applied for convexity exploitation. In this paper, we demonstrate that convex constraints are deleterious. We propose an alternative optimization technique based on genetic algorithms. Genetic programming performs a direct search of the optimal solution without approximations and solution domain restrictions. As a consequence, collaborative multiband sensing can be consistently optimized without limitations. Additionally the genetic optimization exploits the correlation of time-varying channels for fast adaptive convergence