<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1815-5928</journal-id>
<journal-title><![CDATA[Ingeniería Electrónica, Automática y Comunicaciones]]></journal-title>
<abbrev-journal-title><![CDATA[EAC]]></abbrev-journal-title>
<issn>1815-5928</issn>
<publisher>
<publisher-name><![CDATA[Universidad Tecnológica de La Habana José Antonio Echeverría, Cujae]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1815-59282019000200049</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Subspace-Based SNR Estimator for Cognitive Radio and Link Adaptation]]></article-title>
<article-title xml:lang="es"><![CDATA[Estimador de la SNR Basado en el Método del Subespacio para Radio Cognitiva y Adaptación de Enlace]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez Adán]]></surname>
<given-names><![CDATA[Darian]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Torres Gómez]]></surname>
<given-names><![CDATA[Jorge]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Alvarez Cesar]]></surname>
<given-names><![CDATA[Flavia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Technological University of Havana &#8220;José Antonio Echevarría&#8221;  ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Research &amp; Development Telecommunication Institute LACETEL®  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2019</year>
</pub-date>
<volume>40</volume>
<numero>2</numero>
<fpage>49</fpage>
<lpage>61</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1815-59282019000200049&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1815-59282019000200049&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1815-59282019000200049&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Signal-to-Noise Ratio (SNR) parameter represents the main metric to characterize the performance of signal reception. Determining this parameter is of major importance for a wide variety of communication techniques such as spectrum sensing in Cognitive Radio, Link Adaptation, and power allocation. In general, there are two kinds of SNR estimation techniques: Data-Aided (DA) and Blind Estimation (BE). By using Data-Aided estimation (DA), the receiver estimates the SNR based on prior information from the transmitter. On the other hand, by using Blind-Estimation (BE), the receiver does not have any prior-knowledge of transmission parameters. This technique is extremely used for scenarios where transmission parameters are unknown, a common situation on spectrum sensing for non-cooperative applications in Cognitive Radio. However, some reported BE algorithms have been developed exploiting specific properties of some modulation schemes, which also demands some prior knowledge of signal parameters. This work is focused on describing SNR estimation algorithms suitable for several digital and analog modulation schemes. We propose the Subspace-Based SNR estimator for spectrum sensing by using the Energy Detector and Link Adaptation applications. Comparative simulation results regarding estimator performance exhibit the high precision for several channel models. The applicability of this estimator for several analog and digital modulation schemes is also shown as well as proper performance for low SNR levels is obtained, in exchange for higher computational complexity.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La Relación Señal a Ruido representa una de las principales métricas para caracterizar la recepción de una señal. Determinar con la mayor precisión posible este parámetro es de gran importancia para una variedad de técnicas de comunicaciones como: el sensado de espectro en la Radio Cognitiva, la Adaptación de Enlace y el control de potencia. En general, existen dos clasificaciones en cuanto a técnicas de estimación de la SNR: Las técnicas de estimación asistidas por datos (DA) y las técnicas de estimación a ciegas (BE). En la estimación asistida por datos (DA), el receptor estima la SNR basado en el conocimiento previo de los datos enviados por el transmisor. Por otra parte, en las técnicas de estimación a ciegas (BE), el receptor no conoce los parámetros de la transmisión de antemano. Esta técnica resulta de gran utilidad para escenarios donde se desconocen parámetros de la transmisión, situación típica del sensado de espectro en la Radio Cognitiva en escenarios no cooperativos. Sin embargo, algunos de los algoritmos BE han sido desarrollados aprovechando propiedades específicas de algunos esquemas de modulación, lo cual implica el conocimiento previo de los parámetros de la señal. Este trabajo está enfocado en la descripción de algoritmos de estimación de la SNR aplicables a varios esquemas de modulación digital y analógica. Se propone el algoritmo de estimación de la SNR basado en el método del subespacio para aplicaciones de sensado de espectro con el empleo del detector de energía y adaptación de enlace. Resultados comparativos por simulaciones en relación al desempeño del estimador muestran la precisión del mismo para varios modelos de canales. También se muestra la aplicabilidad de este estimador para varios esquemas de modulaciones analógicas y digitales, así como un buen desempeño del mismo en bajos niveles de SNR, a cambio de una mayor complejidad computacional.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Signal-to-Noise Ratio Estimation]]></kwd>
<kwd lng="en"><![CDATA[Blind Estimation]]></kwd>
<kwd lng="en"><![CDATA[Cognitive Radio]]></kwd>
<kwd lng="en"><![CDATA[Energy Detector]]></kwd>
<kwd lng="en"><![CDATA[Link Adaptation]]></kwd>
<kwd lng="es"><![CDATA[Estimación de la Relación Señal a Ruido]]></kwd>
<kwd lng="es"><![CDATA[Estimación a Ciegas]]></kwd>
<kwd lng="es"><![CDATA[Radio Cognitiva]]></kwd>
<kwd lng="es"><![CDATA[Detector de Energía]]></kwd>
<kwd lng="es"><![CDATA[Adaptación de Enlace]]></kwd>
</kwd-group>
</article-meta>
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