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Geometría del ancho de banda d= e un canal de comunicación

 

Bandwidth geometry= of a communication channel

 

Deysi Margoth Guanga Chunata. [1], = Oswaldo Martínez Guashima. [2],<= /span> Mercedes Leticia Lara Freire. [3] & Omar Vinicio Galarza Barrionuevo. [4]

 

Recibido: 10-01-2020 / Revisado: 15-02-2020 /Aceptado: 04-03-2020/ Publicado: 04-04-2= 020

 

Abstract.                                          <= /span>DOI: https://doi.org/10.33262/cienciadigital.v= 4i2.1207

 

The objective of this work is to show the results = of a study carried out with a group of 50 Telecommunications engineering student= s to whom the cognitive construction of geometric elements in the determination = of the bandwidth of a channel, it was applied as a learning test. Mathematical instruction in the epistemic and cognitive dimensions. In this search work = were implement a didactic strategy and a construction methodology, designed on geometric elements to reason about conditional probability. The productions analyzed before and after the instruction. The purpose was to contribute to= the construction of the meaning of the formula programmed in the software or firmware of a network interface, by promoting the ability to carry out the cognitive construction of the communication channel and its behavior in a w= ork setting. Students who developed this skill showed less difficulty in their resolutions and came up with new ideas on optimizing.

Keywords: Geometry, Communication Systems, Bandwidth, Coding

Resumen.

 

Este trabajo tiene por objeto mostrar los resultados de un estudio planteado con= un grupo de 50 estudiantes de ingeniería en Telecomunicaciones a quienes se les aplicó la construcción cognitiva de los elementos geométricos en la determinación del ancho de banda de un canal como una prueba de aprendizaje= , la instrucción Matemática en las dimensiones epistémica y cognitiva. Se implem= entó una estrategia didáctica y una metodología de construcción, diseñada sobre = los elementos geométricos para razonar sobre probabilidad condicional. Las producciones se analizaron antes y después de la instrucción. El propósito = era contribuir a la construcción del significado de la fórmula programada en el software o firmware de una interfaz de red, al favorecer la habilidad de efectuar la construcción cognitiva del canal de comunicación y su comportamiento en un escenario de trabajo. Los estudiantes que desarrollaron esta habilidad, mostraron menores dificultades en sus resoluciones y plante= aron nuevas ideas sobre optimización del canal de comunicación.

P= alabras claves: Geometría, Sistemas de comunicación, Ancho de ban= da, Codificación

Intro= ducción.

En la actualidad, las categorías más utilizadas en la teoría de la comunicació= n y sus aplicaciones han sido la definición de límites fundamentales en la velocidad y confiabilidad de la transferencia de información, la capacidad = del canal y la relación señal / ruido como un indicador determinante de la cali= dad de comunicación prevista.  <= span style=3D'font-size:12.0pt;line-height:115%;font-family:"Times New Roman",se= rif'>Sin lugar a dudas, la principal categoría de la teoría moderna de la informació= n es el concepto de ancho de banda de un canal. (Facchini, 2020)=

En su interpretación, el ancho de banda se mide como la cantidad de datos que = se pueden transferir entre dos puntos de una red en un tiempo específico. Normalmente, el ancho de banda se mide en bits por segundo (bps) y se expre= sa como una tasa de bits. La medición del ancho de banda se realiza normalmente mediante software o firmware en una interfaz de red. Normalmente, para medi= r el ancho de banda, se calcula la cantidad total de tráfico enviado y recibido = en un período de tiempo específico.  CITATION Ama19 \l 2058  (Amado, 2019)

La capacidad del canal se expresó en términos estadísticos a través de la característica matemática introducida de la distribución de probabilidad co= njunta de dos variables aleatorias, denominada cantidad de información. La cantidad máxima de información en la señal en la salida del canal con respecto a la señal en su entrada, donde el máximo se toma sobre todas las posibles distribuciones de probabilidad de la señal en la entrada. La cantidad de información, a su vez, se expresa a través de otra cantidad que se ha utili= zado durante mucho tiempo en termodinámica: la entropía y representa la diferenc= ia entre la entropía de una señal en la salida del canal y su entropía condici= onal si se conoce la señal en la entrada.

Pese a los logros innegables en la teoría de la información, la falta de constructivismo práctico en varias formulaciones de teoremas y los problema= s de desarrollo, el progreso tecnológico visible de todos los tipos de servicios= de comunicación es prácticamente incapaz de ocultar el hecho de la ausencia de= un aumento significativo en la eficiencia específica de los equipos de telecomunicaciones (Castro-Ladino, 2019= ).

Los protocolos del sistema de transferencia de información de canal y capa físi= ca (SPI) son, en gran medida, costosos. Los códigos resistentes a interferenci= as, cuya historia de estudios teóricos y experimentales se han llevado a cabo d= urante más de 70 años, se usan en la práctica en el modo de corrección de errores.= La razón de esto no es solo la complejidad computacional de construir y decodificar estructuras voluminosas en canales de alta velocidad, sino tamb= ién la inaceptabilidad de una probabilidad residual bastante sustancial de decodificación (corrección) con un error en la transmisión de datos y texto= s de programas de computadora.

El desarrollo de la tecnología de las comunicaciones es de naturaleza purament= e extensa. La ganancia de rendimiento se logra casi exclusivamente al mejorar la base tecnológica para la implementación de dispositivos transceptores, así como = al expandir la banda de frecuencia y la potencia del transmisor (que, de hecho, determina la definición matemática del rendimiento).  Esto tiene ciertas consecuencias negativ= as en los aspectos morales, materiales y ambientales. El problema de la compatibilidad electromagnética es cada vez más importante. Los rangos trad= icionales de radiofrecuencias sobrecargados y un pequeño ancho de banda de las líneas= de comunicación metálica forzaron el uso del rango óptico, cuyas posibilidades= tampoco son ilimitadas y requieren de dispositivos de acondicionamiento de señal ad= icionales.

Las tecnologías móviles, que van desde 2G, LTE y hasta el prometedor 5G, sufre cambios drásticos, con la excepción de modificaciones extensas. La órbita geoestacionaria de los satélites de comunicación se acerca a la máxima saturación con objetos espaciales. La tasa de aumento en la demanda de serv= icios de comunicación excede la tasa de aumento en la productividad de SPI. Todo esto, por supuesto, es evidencia de los problemas obvios de la teoría y la práctica de construir sistemas de transferencia de información  (Vesga Ferreira, 2019).

Construcción Cognitiva

Los elementos matemáticos son necesarios y se evidencian como prerrequisitos pa= ra los estudiantes de ingeniería en Telecomunicaciones, por otra parte. La Teo= ría APOE (Acrónimo de Acción, Proceso, Objeto y Esquema) es una teoría constructivista que toma como marco de referencia las ideas de Piaget respe= cto al desarrollo del conocimiento, fundamentalmente rescatando el concepto de abstracción reflexiva y el concepto de esquema. Dubins= ky, quien desarrolla esta teoría, extiende el análisis cognitivo de conceptos matemáticos que se estudian de un nivel escolar, a un nivel de educación superior (García-Martinez, 2017)=

En la teoría APOE, las estructuras mentales que describen el proceso de construcción de un fragmento del conocimiento matemático constituido en cua= tro categorías: acción, proceso, objeto y esquema. Conforme un individuo reflex= iona y trabaja para comprender uno o varios conceptos matemáticos pone en juego mecanismos mentales: interiorización, coordinación, encapsulación y reversi= ón, que son considerados casos particulares de la abstracción reflexiva (GODINO, Indicadores de= la idoneidad didáctica de procesos de enseñanza y aprendizaje de las matemáticas., 2011)

= Figura 1: Estructuras y mecanismos mentales para construir conceptos

= Fuente: Elaboración propia

Al considerar un objeto matemático se realizan acciones sobre objetos previame= nte construidos, relacionándolos con otros conceptos matemáticos que se toman c= omo base para la construcción del nuevo. Las acciones se caracterizan por ser transformaciones que se realizan paso a paso, obedeciendo a estímulos que s= on o se perciben como externos (Dubinsky, 1996)=

Un estudiante ha interiorizado una acción en un proceso si puede realizar una operación interna que hace (o imagina) esencialmente la misma transformación enteramente en su mente, sin necesariamente realizar todos los pasos específicos. (Arnon, 2014)

La teoría APOE proporciona un ciclo de investigación compuesto por tres componentes: el análisis teórico, el diseño y aplicación de instrumentos y = el análisis y verificación de datos, el cual ha sido utilizado con éxito por el Grupo Rumec y otros investigadores (García-Martinez, 2017)=

Sobre la primera componente del método surge la interrogante ¿Cómo representar geométricamente el ancho de banda de un canal? Puede ser que la representac= ión corresponda a una manifestación de algo más profundo, por un lado, definir = el espacio que limita el canal de información y la forma como transmite datos o señales no sólo corresponde a una lista de números.

Metodología.

Al diseñar esta propu= esta didáctica se considera en primer lugar, los conocimientos previos de los alumnos, sus experiencias y el contexto en el que están inmersos. Dado que = la asignatura Redes de Comunicación es común a muchas carreras de ingeniería en telecomunicaciones, el profesor hace preguntas a sus alumnos para involucra= rlos y despertar su interés, en este caso se pregunta a cómo calcular el ancho de banda de un canal de comunicación.

Una forma de determin= ar el ancho de banda basado en la construcción geométrica multidimensional de = la señal y el espacio de interferencia, presentado en la aproximación "plana&qu= ot; en la Figura. 2. Cualquier implementación de una señal aleatoria continua de duración T, cuyo espectro está limitado por la frecuencia F, con está representada por un punto en el espacio dimensional. Considerando un sistema de transmisión en óptimas condiciones estos puntos denominados  se distribuyen equita= tivamente dentro de una hiperesfera con un radio determin= ado por la potencia de señal promedio y la dimensión espacial:

( 1= )

En volumen:              

( 2= )

Donde  es la función gamma para una distribución uniforme de los puntos de señal, pue= de que puede ubicarse aleatoriamente en las n coordenadas: variables aleatorias con media cero y una dispersión igual a S. Con un aumento ilimitado en la dimensión del espacio n, la distribución de puntos se acercará monótonamente a la distribución uniforme. Esta propiedad asintótic= a de uniformidad es la base para construir códigos aleatorios, Una implementación aleatoria de una señal es una forma de canal de la palabra de código de un código aleatorio y se puede obtener de una de dos maneras:

( 3= )

Que representa la descomposición aleatoria en base a funciones  de referencia y tiene un espectro de frecuencia F continuo.

<= span style=3D'font-size:11.0pt;line-height:115%;font-family:"Calibri",sans-ser= if; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:"Times New Roman= "; mso-fareast-theme-font:minor-fareast;mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi; mso-ansi-language:ES;mso-fareast-language:ES;mso-bidi-language:AR-SA'>

( 4= )

Que representa la expansión de Fourier aplicado en el armónico ortogonal en el intervalo T, c= on implementación   t es periódico en T y, si se continúa indefinidamente su repetición, tendrá un espectro discreto limitado anteriormente por un valor F, cuyas mediciones distintas de cero se encuentran en incrementos de   en frecuencia.

Figura 2. Representación geométrica del espacio del sistema de transmisión de información.

Fuente: Elaboración Propia

Ambos métodos las ecuaciones (3) y (4) se pueden usar para describir el rendimiento con la ay= uda de la codificación dada por Shannon. Se aplican   muestras de ruido, cada duración T. Con valores  de . En el transmisor, las secuencias de mensajes se dividen en grupos de k dígitos binarios y para cada grupo la muestra de ruido correspondiente se transmite como una señal. En el extremo receptor, se conocen estas m muestras, y la señal realmen= te recibida (distorsionada por el ruido) se compara con cada una de ellas. La muestra que tiene la desviación estándar más pequeña de la señal recibida s= e toma como la señal transmitida, a partir de la cual se restaura el número binario correspondiente. Esta técnica es equivalente a elegir la señal más probable= .

Las ecuaciones (3), (= 4) junto con la descripción anterior muestran el proceso de construcción y dec= odificación de un código aleatorio. La decodificación se lleva a cabo de acuerdo con la regla, que ahora se llama tradicionalmente "Regla de máxima verosimilitud" (PMP). Con un aumento ilimitado en la longitud del bloq= ue de código (aumento sincrónico en los parámetros k y   corresponde a las palabras de código transmitidas, bajo la influencia de un gaussiano. = Las interferencias se desplazan dentro de esferas de incertidumbre con un radio= :

( 5= )

  y volumen:

( 6= )

De acuerdo con la manifestación de la ley de los grandes números con n creciente, la probabilidad de encontrar puntos desplazados fuera de una esfera con un rad= io  tiende a cero (  valor arbitrariamente pequeño). Las esferas de incertidumbre adquieren una forma = más definida. Shannon los comparó con las bolas de billar, dado que las señales= de palabra de código y la interferencia son independientes entre sí, el radio total del espacio hiperesférico que contiene m esferas de incertidumbre se car= acteriza por el radio y el volumen:

, 

( 7= )

( 8= )

En n→∞ determina el número máximo de esferas disjuntas que se pueden empaquetar en= el volumen   de tal manera que entre ellos, prácticam= ente no hay espacios vacíos:

( 9= )

De acuerdo con la construcción de palabras de código las ecuaciones (3) y (4) ,

( 10)

C revela el resultado= de la tasa de transferencia de información máxima alcanzable en un canal con r= uido aditivo con una confiabilidad arbitrariamente pequeña. Sin embargo, debe tenerse en cuenta que en la lógica de la derivación de la ecuación (10), el valor C es el límite de velocidad del mejor código, usando la regla de máxi= ma verosimilitud al decodificar. Si esto no fuera así y el receptor no tuviera que almacenar= en la memoria muestras de segmentos de implementaciones de señal para su uso en comparaciones de PMF, entonces para Alfo= nso, A. G. (2012). Comprensión lectora y concepciones de estudiantes universitarios sobre enunciados matemáticos. Instituto de Estudios Superiores en Educación, , (17), 2-23.<= /p>

Amado, J. P.-C. (2019). SOLUTION OF A P AND S = WAVE PROPAGATION MODEL USING HIGH PERFORMANCE COMPUTATION. CT&amp;F - Ciencia, Tecnología y Futuro, 9(1); 119-130.

Arnon, I. C. (2014). A framework for research and curr= iculum development in mathematics education. Springer. APOS Theory, doi: 10.1007/978-1-4614-7966-6. Obtenido de A framework for research and curriculum development in mathematics education.

Castro-Ladino, J. R.-B. (2019). Aplicación de metamateriales para estimar índices de refracción. Revista de la Acade= mia Colombiana de Ciencias Exactas, Físicas y Naturales, 43(167); 193-198= .

DÍAZ, C. D. (2014). Razonamiento sobre Probabi= lidad Condicional e implicaciones para la enseñanza de la Estadística. <= span lang=3DEN-US style=3D'mso-ansi-language:EN-US;mso-no-proof:yes'>Andalucía: Epsilon,.

Dorier, J= . L. (2002). The teaching and learning of mathematics at university level. New ICMI Study Series, 7(3), 255-273.

Dubinsky, E. (1996). Aplicación de la perspect= iva piagetiana a la educación matemática universitaria. Educación Matemáti= ca, , 8(3), 24-41.

Facchini, ,. H. (2020). Evaluación de métricas= del comportamiento del tráfico de video en una red experimental multidifusión= . Enfoque UTE; , 11(1); 15-27.

FONT, V., GODINO, J., & D’AMORE, B. (24 de= 10 de 2012). Enfoque Ontosemiótico de las representaciones en Educación Matemática. v. 27, n. 2, p. 3-9. 2007. Obtenid= o de For the learning of mathematics, Montreal,: <http://www.ugr.es/~jgodino/menuing.htm>. Acceso en: 24 oct. 2012.<= o:p>

García-Ma= rtinez, I. &. (2017). The basis step in the construction of the principle of mathematical induction based on APOS theory. The Journal of Mathematical Behavior, 46, 123-143.

GODINO, J. (2011). Indicadores de la idoneidad didáctica de procesos de enseñanza y aprendizaje de las matemáticas. C= ONFERENCIA INTERAMERICANA DE EDUCACIÓN MATEMÁTICA, (págs. p. 1-20.). CONFERENCIA INTERAMERICANA DE EDUCACIÓN MATEMÁTICA,.

GODINO, J., & BATANERO, C. S. (24 de 10 de 2012). Recherches en Didactique des Mathématiques. Obtenido de Grenoble,: <http://www.ugr.es/~jgodino/menuing.htm>.

POLLATESK= , A., WELL, A., KONOLD, C., HARDIMAN, P., & COBB, G. (1987). Understanding Conditional Probabilities. Organizational, Behavior and Human Decision Processes,, n. 40, p. 255-269.

Rafael-Valdivia, G. (2019). Modelo compacto con capacidad de predicción de parámetros físicos para amplificadores de RF. = Revista Facultad de Ingeniería, 28(51); 73-87.

Vesga Ferreira, J. C. (2019). Optimización del= ancho de banda en redes BPL usando las técnicas nucleolus y max-min fairness. <= i>Revista Ingenierías Universidad de Medellín, 18(34); 165-180.

 

PARA CITAR EL ARTÍCULO INDEXADO.

 

 

Guanga Chunata, D. M., Martínez Guashima, O., Lara Freire, M. L= ., & Galarza Barrionuevo, O. V. (2020). Geometría del ancho de banda de un canal de comunicación. Ciencia Digital, 4(2), 77-87. https://doi.org/= 10.33262/cienciadigital.v4i2.1207

 

 


 

 

 

El artículo que se publica es de exclusiva responsabilidad de los autores y no necesariamente reflejan el pensamiento de la Revista Ciencia Digital.

 

El artículo qu= eda en propiedad de la revista y, por tanto, su publicación parcial y/o total en otro medio tiene que ser autorizado por el director de la Revista Ciencia Digital.

 

 

 

 

 



[1] Escue= la Superior Politécnica de Chimborazo, Facultad de Informática y Electrónica. Riobamba, Ecuador. deysi.guanga@espoch.edu.ec

[2] Escuela Superior Politécnica de Chimborazo, Facul= tad de Informática y Electrónica. Riobamba, Ecuador. geova= nny.martinez@espoch.edu.ec

[3] Escuela Superior Politécnica de Chimborazo, Facultad de Informática y Electrónica. Riobamba, Ecuador. leticia.lara@espoch.edu.ec

[4] Universidad de la Fuerzas Armadas, Departamento de Ciencias Exactas. Sangolquí, Ecuador. ovglarza@espe.edu.ec

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