Wideband Spectrum Sensing on Real-Time Signals at Sub-Nyquist Sampling Rates in Single and Cooperative Multiple Nodes

The authors would like to acknowledge the Engineering and Physical Sciences Research Council (EPSRC) in the UK for their support of this work with Grant No. EP/L024241/1. Mark D. Plumbley was partly supported by a Leadership Fellowship (EP/G007144/1) from the UK EPSR

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

Detección de espectro en banda ancha Sub-Nyquist para redes Radio Cognitiva: compleción de matrices mediante valores semilla

Introduction: Cognitive Radio (CR) makes efficient use of the radio resource, for this it performs Spectrum Sensing (SS) in order to identify the available spectrum. But due to the rapid evolution of transceivers, microelectronics and high propagation frequencies, it is necessary for SS algorithms to be applied in frequency bands in CR and for sampling below the Nyquist rate. Objective: Adapt an algorithm for Wideband Sub-Nyquist Spectrum Detection (WBSS) for CR networks using Matrix Completion (MC) integrating seed values from known samples, in order to complete the unsampled inputs of the band to evaluate, reconstruct the signals and the identify the available spectrum. Method: An adaptation to the Interest Zone Matrix Approximation (IZMA) algorithm was carried out, for this purpose the reconstruction stage is designed and a narrow band spectrum sensing method is chosen to form the detector bank; the algorithm called IZMA_SV is evaluated at the simulation level, therefore deterministic signals are reconstructed in different SNRs and the channel status is identified as busy or free. Results: The simulations indicate that the adapted algorithm shows differences between the known values of the sampling matrix M and the recovered matrix X in SNRs lower than -8 dB, while the difference tends to zero in SNRs greater than 2 dB. Conclusions: The IZMA-SV algorithm manages to reduce the number of operations to arrive at the approximate matrix X, reconstructing signals sampled at 75% of the Nyquist rate and even with a sampling of 20% the characteristics of the signal that make possible the detection of wideband spectrum.Introducción: La Radio Cognitiva (CR) hace un uso eficiente del recurso radioeléctrico, para ello realiza la Detección de Espectro (SS) con el fin de identificar el espectro disponible. Pero debido a la rápida evolución de los transceptores, la microelectrónica y las altas frecuencias de propagación, se hace necesario que en CR se apliquen algoritmos de SS en bandas de frecuencia y se realice un muestreo inferior a la tasa de Nyquist. Objetivo: Adaptar un algoritmo para Detección de Espectro Sub-Nyquist en Banda Ancha (WBSS) para redes de CR mediante la Compleción de Matrices (MC) que integra valores semilla a partir de las muestras conocidas, con el fin de completar las entradas no muestreadas de la banda a evaluar, reconstruir las señales e identificar el espectro disponible. Metodología: Se realizó una adaptación al algoritmo Aproximación Matricial de la Zona de Interés (IZMA), para ello se diseña la etapa de reconstrucción y se elige un método de detección de espectro en banda estrecha para conformar el banco de detectores; el algoritmo que se denomina IZMA_SV es evaluado a nivel de simulación, por tanto se reconstruyen señales determinísticas en diferentes SNR y se identifica el estado del canal como ocupado o libre. Resultados: Las simulaciones indican que el algoritmo adaptado presenta diferencias entre los valores conocidos de la matriz de muestreo M y la matriz recuperada X en SNR inferiores a -8 dB, mientras que la diferencia tiende a cero en SNR superiores a 2 dB. Conclusiones: El algoritmo IZMA-SV logra reducir el número de operaciones para llegar a la matriz aproximada X, reconstruyendo señales muestreadas al 75% de la tasa Nyquist y aún con un muestreo del 20% se mantienen las características de la señal que hacen posible la detección de espectro en banda ancha.&nbsp