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Este módulo contiene la teoría correspondiente al método de Ortogonalización Gram-Schmidt aplicado para el proceso de la comunicación digital. Se explicarán los pasos necesarios para generar las bases ortogonales dados ciertos parámetros de la señal. Finalmente, teniendo las bases calculadas, se explicará el procedimiento para hallar la constelación correspondiente.

ORTOGONALIZACIÓN GRAM-SCHMIDT Y TEORÍA BÁSICA DE LAS CONSTELACIONES

González C. Y. Venuska

Mezoa R. Mariangela

Resumen

Este módulo contiene la teoría correspondiente al método de Ortogonalización Gram-Schmidt aplicado para el proceso de la comunicación digital. Se explicarán los pasos necesarios para generar las bases ortogonales dados ciertos parámetros de la señal. Finalmente, teniendo las bases calculadas, se explicará el procedimiento para hallar la constelación correspondiente.

En matemáticas, el concepto de Ortogonalidad está referido al de Perpendicularidad . Se dice que dos vectores pertenecientes a cierto espacio vectorial (V) son ortogonales si se cumple la condición de que el producto escalar de ellos da cero , es decir:

Sean : x V y V Si : x , y = x y = 0 Entonces : x y alignl { stack { size 12{ ital "Sean":} {} #size 12{x in V} {} # size 12{y in V} {} #{} # size 12{ ital "Si":} {} #size 12{ langle x,y rangle =x cdot y=0} {} # {} #size 12{ ital "Entonces":} {} # size 12{x ortho y} {}} } {}

A partir de un conjunto de vectores linealmente independientes se puede construir un nuevo conjunto de vectores ortonormales (Que cumplan con las condiciones de ortogonalidad y norma vectorial). Esto se conoce como el método de Ortogonalización Gram-Schmidt (G-S). Pero, ¿cómo aplicamos este concepto para un sistema de comunicación digital?

Ortogonalización gram-schmidt

Supongamos que se tiene una señal Si(t) que representa a un símbolo m i . Se estima que esta señal pase por el receptor que está encargado de obtener cada símbolo de la misma. Sin embargo, es evidente que al pasar por el canal, la señal se contaminará debido a la existencia de ruido en el sistema. En una condición ideal, el resultado sería el siguiente:

Al introducir ruido (AWGN) en el sistema, quedaría como sigue:

{}

Sistema de recepción con introducción de ruido AWGN.

La segunda situación ocasiona que a la salida del receptor no se obtiene el símbolo m i como tal, más bien se obtiene un estimado del símbolo original.

Es en este punto en donde entra el concepto de ortogonalización G-S: La señal Si(t) puede expresarse en función de un conjunto finito de bases (o vectores) ortonormales ( U ), de forma tal que cada forma de onda estaría relacionada con un coeficiente que llamaremos s (Una señal de energía). Matemáticamente tendríamos esto:

Si ( t ) = i = 1 n s ij . U j ( t ) size 12{ ital "Si" \( t \) = Sum cSub { size 8{i=1} } cSup { size 8{n} } {s rSub { size 8{ ital "ij"} } "." U rSub { size 8{j} } \( t \) } } {}

Es decir, a cada símbolo m i se le asocia una forma de onda s. Si desarrollamos la fórmula anterior, para todos los símbolos posibles , tendríamos un sistema de ecuaciones como sigue:

s 1 ( t ) = s 11 . U 1 ( t ) + s 12 . U 2 ( t ) + s 13 . U 3 ( t ) + . . . + s 1n . U n ( t ) s 2 ( t ) = s 21 . U 1 ( t ) + s 22 . U 2 ( t ) + s 23 . U 3 ( t ) + . . . + s 2n . U n ( t ) s 3 ( t ) = s 31 . U 1 ( t ) + s 32 . U 2 ( t ) + s 33 . U 3 ( t ) + . . . + s 3n . U n ( t ) s m ( t ) = s m1 . U 1 ( t ) + s m2 . U 2 ( t ) + s m3 . U 3 ( t ) + . . . + s mn . U n ( t ) alignl { stack { size 12{s rSub { size 8{1} } \( t \) =s rSub { size 8{"11"} } "." U rSub { size 8{1} } \( t \) +s rSub { size 8{"12"} } "." U rSub { size 8{2} } \( t \) +s rSub { size 8{"13"} } "." U rSub { size 8{3} } \( t \) + "." "." "." +s rSub { size 8{1n} } "." U rSub { size 8{n} } \( t \) } {} #s rSub { size 8{2} } \( t \) =s rSub { size 8{"21"} } "." U rSub { size 8{1} } \( t \) +s rSub { size 8{"22"} } "." U rSub { size 8{2} } \( t \) +s rSub { size 8{"23"} } "." U rSub { size 8{3} } \( t \) + "." "." "." +s rSub { size 8{2n} } "." U rSub { size 8{n} } \( t \) {} # s rSub { size 8{3} } \( t \) =s rSub { size 8{"31"} } "." U rSub { size 8{1} } \( t \) +s rSub { size 8{"32"} } "." U rSub { size 8{2} } \( t \) +s rSub { size 8{"33"} } "." U rSub { size 8{3} } \( t \) + "." "." "." +s rSub { size 8{3n} } "." U rSub { size 8{n} } \( t \) {} #dotsvert {} # s rSub { size 8{m} } \( t \) =s rSub { size 8{m1} } "." U rSub { size 8{1} } \( t \) +s rSub { size 8{m2} } "." U rSub { size 8{2} } \( t \) +s rSub { size 8{m3} } "." U rSub { size 8{3} } \( t \) + "." "." "." +s rSub { size 8{ ital "mn"} } "." U rSub { size 8{n} } \( t \) {}} } {}

El objetivo en el segundo sistema mostrado en la Figura 1 es el de obtener el estimado que más se aproxime al valor real. Esto se hace minimizando la energía de la señal de error entre el símbolo original y el estimado:

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Source:  OpenStax, Laboratorio digital interactivo. OpenStax CNX. Feb 09, 2011 Download for free at http://cnx.org/content/col11274/1.1
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