Centralized Spectrum Broker and Spectrum Sensing with Compressive Sensing techniques for resource allocation in Cognitive Radio Networks

RESUMENLa radio cognitiva es una de las técnicas más prometedoras para optimizar el uso del espectro. Sin embargo, la gran cantidad de información espectral que es necesario analizar para identificar y asignar porciones espectrales hace que se incrementen los tiempos de asignación de canal debido al previo procesamiento de los datos y, por lo tanto, no sea posible ofrecer servicio a todos los dispositivos que lo requieran. El muestreo comprimido, por su parte, es una técnica que permite la reconstrucción de señales dispersas o compresibles usando un número menor de muestras que las requeridas por el criterio de Nyquist. En este trabajo se presenta un nuevo modelo que usa muestreo comprimido multiespectral para el sensado del espectro radioeléctrico en radio cognitiva, que mejora los tiempos de sensado y asignación de canal al disminuir el tamaño de los datos necesarios para reconstruir la información espectral de potencia a distintas bandas. Este modelo se basa en arquitecturas que usan muestreo comprimido para analizar imágenes multiespectrales. Se propone la operación de un gestor espectral centralizado que mediante patrones binarios selecciona o descarta los datos de potencia de diferentes radios definidos por software ubicados en diferentes posiciones geográficas. El gestor espectral, con base en las muestras tomadas y aplicando las técnicas de muestreo comprimido multiespectral, reconstruye un cubo de datos con la información de potencia transmitida y frecuencia de operación de todos los usuarios. Los resultados muestran que es posible esta reconstrucción utilizando solo 50% de las muestras generadas por todos los dispositivos, y que la información se puede almacenar usando solo 6,25% del total de datos originales.Palabras clave: Muestreo Comprimido, radio cognitiva, sensado espectral. ABSTRACT

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“…Número y probabilidad de colisiones con respecto a la duración de los paquetes de control. Tomado de [4].…”

Section: Positions the Centralized Manager Reconstructs A Data Cube unclassified

“…En [4] se muestra un ejemplo de la incidencia que tiene el aumento de la cantidad de datos procesados durante el sensado espectral en la adecuada asignación de espectro a los usuarios cognitivos en un escenario…”

Section: Introductionunclassified

Modelo de muestreo comprimido multiespectral para radio cognitiva

Marín

Betancur

Argüello

2018

Ingeniare. Rev. chil. ing.

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“…In practical, the usage of licensed spectrum is very low compared to the usage of unlicensed spectrum band. The CR is an intelligent radio, which can be programmed in any given network, by using the greatest wireless channel available in its neighborhood as one key technology to activate the utilization of spectrum resource.We can use dynamic spectrum access technology that can be improves the excellent performance of wireless network communication and make it as reliable networks and robust, however the nodes are mobile and has less power sources [6] [7][8] [9].…”

Section: Cognitive Radio and Intelligent Antennamentioning

confidence: 99%

“…Marin Alfonso et al [18] have proposed a Compressive Sensing technique named Finite Rate of Innovation (FRI) in a Cognitive Radio Network through centralized Spectrum Management based Spectrum Broker approach for spectrum sensing. Spectrum Brokers are coordinating the processes and algorithms related to spectrum management, based on spectrum information shared by SDR users, which perform the spectrum sensing process.…”

Section: Literature Surveymentioning

confidence: 99%

Appropriate Channel Selection for Dynamic Resource Allocation with Priority Scheduling Approach in Multi-hop Cognitive Radio Networks

Santhamurthy¹,

Parasuramane²

2017

IJIES

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Today's wireless communication systems required groundbreaking solution to meet scarcity of radio spectrum. Cognitive radio (CR) is an emerging technology which provides futuristic solution and improving the precious likely resources utilization. Cognitive radio network (CRN) is an intellectual wireless communication system as well as aware of its environment. In CRN, we have sensing problem by multiple nodes with changing computational power, spectrum allocation and range of sensing. The secondary user (SU) plays major role of primary channel sensing and appropriate unused channel selection for communication. In this paper we focused on optimum resource allocations and priority based scheduling for primary user (PU) and secondary user (SU) to exploit the performance of the entire network by reducing the interference at PU and also exploiting the throughput and decreasing the risk of overlying the coverage of CRNs. The effectiveness of dynamic resource allocation priority based scheduling (DRAPBS-CRN) algorithm is compared with dynamic resource allocation (DRA-CRN) algorithm and is found to be 26% more efficient using ns-2 simulation. This proposed DRAPBS-CRN algorithm also will avoid collisions and perform smart data transmission in CRN.

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“…This spectrum sensing problem, ie., the spectral sensing speed, is one of the most challenging issues in cognitive radio systems today and in the near future because of the enormous increase in the number of devices trying to access the spectrum. As an example, [3] shows the effect of the increase in the amount of data processed during the spectral sensing that generates data package collisions when several cognitive devices send packages with spectral information to a centralized server (if a device sends its data packet and the server is busy, a collision occurs). The larger the packet, more collisions and worse quality of the service.…”

Section: Introductionmentioning

confidence: 99%

Compressive multispectral model for spectrum sensing in cognitive radio networks

Marin

Martinez

Betancur

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Cognitive Radio (CR) is one of the most promising techniques for optimizing the spectrum usage. However, the large amount of data of spectral information that must be processed to identify and assign spectral resources increases the channel assignment times, therefore worsening the quality of service for the devices using the spectrum. Compressive Sensing (CS) is a digital processing technique that allows the reconstruction of sparse or compressible signals using fewer samples than those required traditionally. This paper presents a model that addresses the Spectral Sensing problem in Cognitive Radio using Compressive Sensing as an effective way of decreasing the number of samples required in the sensing process. This model is based on Compressive Spectral Imaging (CSI) architectures where a centralized spectrum manager selects what power data must be delivered by the different wireless devices using binary patterns, and builds a multispectral data cube image with the geographical and spectral data power information. The results show that this multispectral data cube can be built with only a 50% of the samples generated by the devices and, therefore reducing the data traffic dramatically.

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Centralized Spectrum Broker and Spectrum Sensing with Compressive Sensing techniques for resource allocation in Cognitive Radio Networks

Cited by 11 publications

References 15 publications

Modelo de muestreo comprimido multiespectral para radio cognitiva

Modelo de muestreo comprimido multiespectral para radio cognitiva

Appropriate Channel Selection for Dynamic Resource Allocation with Priority Scheduling Approach in Multi-hop Cognitive Radio Networks

Compressive multispectral model for spectrum sensing in cognitive radio networks

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