Compressive Sensing (CS) is a new paradigm for signal acquisition and processing, which integrates sampling, compression, dimensionality reduction and optimization, which has caught the attention of a many researchers; SC allows the reconstruction of dispersed signals in a given domain from a set of measurements could be described as incomplete, due to that the rate at which the signal is sampled is much smaller than Nyquist's rate. This article presents an approach to address methodological issues in the field of processing signals from the perspective of SC.
The video streaming service, besides offering interactivity by users' actions through pauses, backwards, and forwards, is the highest generator of traffic on data networks. Hence, it is necessary to characterize this behavior for network dimensioning. This article presents the conceptual models of the service and a lexical analyzer whose function is the automatic extraction of the different frames of traffic streams and identification of interactivity processes. Through the lexical analyzer, the information from each of the components of the service such as the I, P, and B frames is extracted; and so is the audio component. This means that the lexical analyzer provides granularity to the characterization process. Thus, it is expected that the lexical tool allows studying the behavior of traffic based on distribution functions obtained in situ, not relying on assumptions or studies from other researchers.
La principal característica del servicio de video bajo demanda a través de la tecnología de video streaming es el consumo de grandes anchos de banda, por lo que los planificadores de redes deben considerar este servicio en el momento de dimensionar dichas redes. Para alcanzar esta meta, una de las herramientas es la ingeniería de tráfico y sus modelos asociados. Así, este artículo presenta un modelo de tráfico basado en la simulación por eventos discretos y desarrollado a través del software de código abierto y de investigación en redes, denominado ns-3. Se describen las abstracciones de los elementos de red, y el proceso de construcción de diferentes escenarios de evaluación, independiente de las características del servicio que se desee analizar, y la recolección y visualización gráfica de estadísticas. Finalmente se presentan los análisis de los parámetros de desempeño que permiten obtener las características mínimas de red para soportar el servicio.
* Autor a quien debe ser dirigida la correspondenciaRecibido Sep. 30, 2016; Aceptado Dic. 1, 2016; Versión final Ene. 10, 2017, Publicado Jun. 2017 Resumen Este artículo aborda el problema de sensado de espectro local de banda ancha realizado por Radios Cognitivos de múltiples antenas, basado en muestreo sub-Nyquist uniforme en el dominio disperso y compleción de matrices. En el escenario de banda ancha, uno de los mayores problemas para realizar el sensado de espectro es el gran número de muestras a procesar cuando se realiza el muestreo de señales multibanda a tasa iguales o superiores a la tasa de Nyquist. Esto genera grandes tiempos de detección, altos consumos de energía y la necesidad de altas capacidades de procesamiento en los Dispositivos de Radio Cognitivo. Los resultados de simulación demuestran que el método propuesto permite realizar el sensado de espectro de manera eficiente, mejorando el desempeño del sensado.
Palabras clave: compleción de matrices; muestreo sub-Nyquist; sensado de espectro; radio cognitivo
Local Wideband Spectrum Sensing Algorithm for Multiantenna Cognitive Radios based on Matrix Completion and Uniform Sub-Nyquist Sampling in Sparse Domain
AbstractThis article addresses the problem of spectrum local broadband sensing by Cognitive Radio of multiple antennas, based on matrix completion and uniform sub-Nyquist sampling in sparse domain. In the wideband environment, one of the biggest problems for spectrum sensing is the large number of samples to be processed when a multiband signal is sampled at or above the Nyquist rate. This generates large detection times, high-energy consumptions and the need for high processing capabilities in Cognitive Radio Devices. The simulation results show that the proposed method allows the efficient sensing of spectrum, improving performance sensing.
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