Tema7.Sistema_Comunicacion_Digital_Lineal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MANUAL DE LABORATORIO DE COMUNICACIONES DIGITALES: PYTHON\n",
    "F. Javier Payán Somet, Juan José Murillo-Fuentes, José Carlos Aradillas Jaramillo \\\n",
    "_Departamento de Teoría de la Señal y Comunicaciones_ \\\n",
    "_Escuela Técnica Superior de Ingeniería_ \\\n",
    "_Universidad de Sevilla_ \n",
    "\n",
    "# Tema 7. Sistema de Comunicación Digital Lineal\n",
    "\n",
    "**Este notebook contiene código del Tema 7**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/gapsc-us/labcomdig/blob/main/Tema7.Sistema_Comunicacion_Digital_Lineal.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inicialización"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python ≥3.5 is required\n",
    "import sys\n",
    "assert sys.version_info >= (3, 5)\n",
    "\n",
    "# Numpy ≥1.16 is required\n",
    "import numpy as np\n",
    "assert np.__version__ >= \"1.16\"\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "# Ignore useless warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\") #Ignorar warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sistema de comunicación digital binario"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las funciones que se describen aquí están en el fichero labcomdig.py y se definieron en temas anteriores, se usan a continuación por código 7.1. Se incluyen aquí para recordarlas, y para facilitar la ejecución en google colab, pero si se dispone del fichero labcomdig.py bastaría con ejecutar en vez de incluir las 3 siguientes funciones.\n",
    "\n",
    "**from labcomdig import transmisorpam, Qfunct** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transmisorpam(Bn,Eb,M,p,L):\n",
    "    '''\n",
    "    [Xn,Bn,An,phi,alfabetopam] = transmisorpam(Bn,Eb,M,p,L) \n",
    "    \n",
    "    Entradas:    \n",
    "     Bn = Secuencia de dígitos binarios\n",
    "     Eb = Energía media por bit transmitida en Julios\n",
    "     M  = Número de síímbolos del código PAM\n",
    "     p  = Pulso paso de baja o paso de banda \n",
    "     L  = Número de puntos a utilizar en la representación de un símbolo\n",
    "    \n",
    "    Devuelve:\n",
    "     Xn = la señal de información (discreta)\n",
    "     Bn = La secuencia de dígitos binarios realmente transmitidos\n",
    "     An = La secuencia de niveles de amplitud transmitidos\n",
    "   \t phi = Pulso básico real normalizado (energía unidad)\n",
    "     alfabetopam = Los niveles de amplitud asociados a cada símbolo\n",
    "    '''    \n",
    "    #Comprobación de parámetros de entrada\n",
    "    p=p.squeeze()\n",
    "    if len(Bn)<1 or Eb<=0 or M<2 or p@p==0 or L<1:\n",
    "        raise Exception('Error: revise los parámetros de entrada')\n",
    "    \n",
    "    # Se obtienen en primer lugar los niveles asociado a cada símbolo ¿Cuántos bits hay en cada sí­mbolo?\n",
    "    k = int(np.ceil(np.log2(M)))   \n",
    "    M = 2**(k) # Se Ajusta M a una potencia de dos\n",
    "    \n",
    "    # El alfabeto [Ver la ecuación (4.21)] \n",
    "    alfabetopam = np.sqrt(3*Eb*np.log2(M)/(M**2-1))*(2*(np.arange(M))-M+1)\n",
    "    \n",
    "    # Si Bn no tiene una longitud múltiplo de k, se completa con ceros\n",
    "    Nb = len(Bn)  # Número de bits a transmitir, actualizado\n",
    "    Bn = Bn.squeeze().astype(int) #Comprobación de int y dimensiones   \n",
    "    Bn = np.r_[Bn,np.zeros(int(k*np.ceil(Nb/k)-Nb)).astype(int)] #\n",
    "    Nb = len(Bn)  # Número de bits a transmitir tras la corrección\n",
    "    Ns = Nb//k # Número de símbolos a transmitir\n",
    "    \n",
    "    # La secuencia generada\n",
    "    if M>2:\n",
    "        An = alfabetopam[gray2de(np.reshape(Bn,[Ns,k]))]\n",
    "    else:\n",
    "        An = alfabetopam[Bn]\n",
    "    \n",
    "    # Comprobación de las longitudes y otros datos del pulso suministrado para \n",
    "    # hacer que el número de muestras del mismo sea efectivamente L\n",
    "    Ls = len(p)\n",
    "    if Ls<L:\n",
    "        p = np.r_[p, np.zeros(L-Ls)]\n",
    "    elif Ls>L:\n",
    "        print('La duración del pulso se trunca a {} muestras'.format(str(L)))\n",
    "        p = p[:L] #Debe modificarse si se quiere un pulso de más de L muestras \n",
    "    # Se normaliza la energía del pulso para obtener la base del sistema\n",
    "    phi = p / np.sqrt(p@p) \n",
    "       \n",
    "    # Obtención del tren de pulsos, Xn = np.kron(An,phi) ó\n",
    "    Xn = np.reshape(np.reshape(An,[Ns,1])*phi,[Ns*L,]) #Debe modificarse si se quiere un pulso de más de L muestras\n",
    "    return [Xn,Bn,An,phi,alfabetopam]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También se necesita, en la función anterior y también presentada en un tema anterior:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gray2de(b):\n",
    "    '''\n",
    "    Convierte cada columna de la matriz formada por dígitos binarios b en un vector\n",
    "        fila de los valores decimales correspondientes, aplicando codificación de Gray.\n",
    "    '''\n",
    "    if not isinstance(b, (np.ndarray, list, tuple)):  # se comprueba que no sea np.array, lista o tupla\n",
    "       raise Exception('Error: la entrada no es un array, lista o tupla')\n",
    "    b = np.array(b)    # Esto es para que admita también listas y tuplas\n",
    "    c = np.zeros_like(b); c[:,0] = b[:,0]\n",
    "    for i in range(1,np.shape(b)[1]):\n",
    "        c[:,i] = np.logical_xor(c[:,i-1], b[:,i])\n",
    "    c = np.fliplr(c) # Convierte los bits menos significativos en los más significativos\n",
    "    #Comprueba un caso especial.\n",
    "    [n,m] = np.shape(c) \n",
    "    if np.min([m,n]) < 1:\n",
    "        d = []\n",
    "        return\n",
    "    d = c @ 2**np.arange(m)\n",
    "    return d "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También se necesita, en el código abajo, la función Qfunct, introducida en temas anteriores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Qfunct(x):\n",
    "    ''' \n",
    "     y = Qfunct(x) evalúa la función Q en x.\n",
    "    Donde y = 1/sqrt(2*pi) * integral desde x hasta inf de exp(-t^2/2) dt\n",
    "    '''\n",
    "    from scipy.special import erfc \n",
    "    from numpy import sqrt\n",
    "    y=(1/2)*erfc(x/sqrt(2)) \n",
    "    return y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Código 7.1** Sistema de comunicación digital binario."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Inicialización\n",
    "Nb = 10**6 # Nb = Nº de bits a transmitir\n",
    "Eb = 9 # Eb = Energía media transmitida en Julios\n",
    "M = 2\n",
    "L = 4 # L =  Nº de puntos que vamos a utilizar para transmitir un bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definición del pulso básico. \n",
    "p = np.ones(L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Secuencia binaria generada por la fuente\n",
    "Bn = np.random.randint(2,size=Nb)  # Bn = Secuencia de dígitos binarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Transmisor PAM\n",
    "[Xn,Bn,An,phi,alfabeto] = transmisorpam(Bn,Eb,M,p,L)\n",
    "# Actualiza el valor de Nb\n",
    "Nb = len(Bn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Receptor Vectorial\n",
    "# Alternativa 1) receptor vectorial usando filtros\n",
    "#hr = np.flipud(phi) #Respuesta impulsiva del filtro adaptado\n",
    "#yn = np.convolve(hr,Xn,'valid') # Salida del filtro adaptado\n",
    "#sn = yn[np.arange(0,Nb*L,L)] # El resultado de muestrear la señal anterior\n",
    "     \n",
    "# Alternativa 2) receptor vectorial usando correlación\n",
    "sn = np.reshape(Xn,[Nb,L]) @ phi "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%%Representación de la Constelación\n",
    "# Se obtienen valores únicos\n",
    "sx = np.unique(sn)\n",
    "\n",
    "# Distancia mínima de la constelación\n",
    "dmin = abs(sx[0]-sx[1])\n",
    "\n",
    "# Representamos\n",
    "xmax = max(sx)+max(sx)/10+0.5\n",
    "xmin = -(abs(min(sx))+abs(min(sx))/10)-0.5\n",
    "plt.figure(71)\n",
    "plt.plot([xmin,xmax],[0,0],'b-'); # Se representan los ejes\n",
    "plt.plot(sx,np.zeros(len(sx)), 'r*') # Se representa la constelación\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Estimación mediante simulación de Monte Carlo de la tasa de error de bit\n",
    "\n",
    "SNRdb = np.arange(20)  # SNR en dB donde estimar la BER estimada por MC\n",
    "\n",
    "# Inicialización\n",
    "berSimulado = np.zeros(len(SNRdb))\n",
    "\n",
    "for ii in range(len(SNRdb)):\n",
    "    # Generamos ruido\n",
    "    SNR = 10**(SNRdb[ii]/10) # relación señal ruido en unidades naturales\n",
    "    varzn = Eb/(2*SNR) #varianza del ruido\n",
    "    # El ruido del canal\n",
    "    Wn = np.sqrt(varzn)*np.random.randn(np.size(Xn))\n",
    "    \n",
    "    # Salida del Canal AWGN: señal más el ruido\n",
    "    rn = Xn + Wn\n",
    "    \n",
    "    #Salida del Receptor Vectorial\n",
    "    #Alternativa 1, receptor vectorial basado en filtros\n",
    "    #vn = np.convolve(hr, rn, 'valid') # Filtrado por filtro adaptado\n",
    "    #r = vn[np.arange(0,Nb*L,L)]  # Se muestrea\n",
    "\n",
    "    #Alternativa 2, receptor vectorial basado en correlador\n",
    "    r = np.reshape(rn,[Nb,L]) @ phi  \n",
    "\n",
    "    #Salida del Detector. El umbral está situado en sx(1)+sx(2))/2\n",
    "    correctos = (r>(sx[0]+sx[1])/2) == (Bn) #ó = np.equal(r>(sx[0]+sx[1])/2,Bn)\n",
    "    \n",
    "    #Estimamos la tasa de error de bit para esa SNR\n",
    "    errores = Nb-np.sum(correctos)\n",
    "    berSimulado[ii]=errores/Nb #BER para SNRdb[ii]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Cálculo de BER teórica    \n",
    "SNRdb2 = np.arange(20, step=0.1)  #SNR donde calcular la BER teórica\n",
    "SNR2 = 10**(SNRdb2/10) #Paso a unidades naturales\n",
    "berTeorico = Qfunct(np.sqrt(2*SNR2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%% Representación de las probabilidades de error\n",
    "plt.figure(72,figsize = (10,6))\n",
    "plt.semilogy(SNRdb,berSimulado,'r*');\n",
    "plt.semilogy(SNRdb2,berTeorico,'k-');\n",
    "plt.axis([1, 16, 10**(-8), 1]), plt.grid()\n",
    "plt.xlabel('Relación señal/Ruido $E_b/N_0$ (dB)')\n",
    "plt.ylabel('Probabilidad de error de bit ($P_b$)')\n",
    "plt.legend(['Simulado','Teórico'],loc='best') # Crea leyenda\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fin código 7.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Sistema de comunicación digital M-PAM\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este tema se presentan las siguientes funciones:\n",
    "\n",
    "**Código 7.2** Función detecta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detecta(r, alfabeto):\n",
    "    '''\n",
    "    An = detecta(r, alfabeto)\n",
    "    r = secuencia a la entrada del detector, con estimaciones de s_i\n",
    "    alfabeto = tabla con los niveles de amplitud/sí­mbolos  \n",
    "    \n",
    "    Genera:\n",
    "    An = una secuencia de sí­mbolos pertenecientes al alfabeto de acuerdo con\n",
    "    una regla de distancia euclidiana mí­nima (mí­nima distancia)\n",
    "    '''\n",
    "    \n",
    "    # Longitud de la secuencia                \n",
    "    N = len(r)\n",
    "    \n",
    "    # Inicializa\n",
    "    An = np.zeros(N)\n",
    "    \n",
    "    for i in range(N):\n",
    "        ind = np.argmin(abs(r[i]-alfabeto)) #ind = np.where(abs(r[i]-alfabeto) == np.amin(abs(r[i]-alfabeto)))\n",
    "        An[i] = alfabeto[ind]\n",
    "    \n",
    "    return An"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**Código 7.3** Función detecta Sin el Bucle For."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def detectaSBF(r,alfabeto):\n",
    "    '''\n",
    "    An = detectaSBF(r,alfabeto)\n",
    "    r = secuencia a la entrada del detector, con estimaciones de s_i\n",
    "    alfabeto = tabla con los niveles de amplitud/símbolos  \n",
    "    \n",
    "    Genera:\n",
    "    An = una secuencia de símbolos pertenecientes al alfabeto de acuerdo con\n",
    "    una regla de distancia euclidiana mínima (mínima distancia)\n",
    "    '''\n",
    "    \n",
    "    # Obtiene el índice respecto al alfabeto\n",
    "    ind = np.argmin(np.abs(np.repeat(np.expand_dims(r,1),len(alfabeto),1) - alfabeto), 1)\n",
    "    \n",
    "    # Genera la secuencia de niveles detectados\n",
    "    An = alfabeto[list(ind)]\n",
    "    \n",
    "    return An"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Código 7.4** Función simbolobit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simbolobit(An,alfabeto):\n",
    "    '''\n",
    "    Bn = simbolobit(An, alfabeto)\n",
    "    An = secuencia de símbolos pertenecientes al alfabeto\n",
    "    alfabeto = tabla con los símbolos utilizados en la transmisión \n",
    "    Bn = una secuencia de bit, considerando que los símbolos se habían\n",
    "    generado siguiendo una codificación de Gray\n",
    "    '''\n",
    "    \n",
    "    k = np.log2(len(alfabeto)) # bits por símbolo\n",
    "    \n",
    "    if k>1:\n",
    "        distancia = abs(alfabeto[0]-alfabeto[1])\n",
    "        indices   = np.round((An-alfabeto[0])/distancia)\n",
    "        Bn        = np.reshape(de2gray(indices,k),[int(k*len(An))])\n",
    "    else:\n",
    "        Bn = ((An/max(alfabeto))+1)/2\n",
    "    \n",
    "    return Bn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En esta función se hace una llamada a la función de2gray utilizando la librería *sympy*.\n",
    "Su código es el siguiente:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Código 7.5** Función de2gray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def de2gray(d,n):\n",
    "    \"\"\"\n",
    "    b = de2gray(d,n)\n",
    "    Convierte un número decimal, d, en un vector binario, b, de longitud n\n",
    "    \"\"\"   \n",
    "    c = np.zeros([len(d),int(n)])\n",
    "    for i in range(int(n)): \n",
    "        d, c[:,i] = np.divmod(d,2)\n",
    "        \n",
    "    c = np.fliplr(c); \n",
    "    b = np.zeros_like(c); \n",
    "    b[:,0] = c[:,0]; aux = b[:,0]\n",
    "    for i in range(1,int(n)):\n",
    "        b[:,i] = np.logical_xor(aux, c[:,i])\n",
    "        aux = np.logical_xor(b[:,i], aux) \n",
    "        \n",
    "    return np.reshape(b,[-1]).astype(np.int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El siguiente código es una extensión del anterior para modulaciones M-arias, ejecutándose para varios valores de M\n",
    " \n",
    "**Código 7.6** Sistema de comunicación digital M-PAM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Inicialización\n",
    "verbose = 1 # Para indicar por pantalla el progreso de la ejecución\n",
    "L = 4 #Número de muestras por símbolo transmitidas\n",
    "Nb = 10**5  #Número de bits a transmitir\n",
    "Eb = 9 #Energía promedo de bit\n",
    "p = np.ones(L) # definamos el pulso básico\n",
    "\n",
    "#Colores RGB para representa curvas:\n",
    "codigocolor = np.array([[0,0,0], [0,1,0], [0,0,1], [1,0,0], [1,0,1], [0,1,1]])\n",
    "jj=0 # variable auxiliar para bucle for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Simulando sistema PAM con M=2\n",
      " Simulando SNR = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, \n",
      "Calculando tasa de error de símbolo teórica\n",
      "Calculando tasa de error de bit teórica\n",
      "Representando resultados\n",
      "\n",
      "\n",
      "Simulando sistema PAM con M=4\n",
      " Simulando SNR = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, \n",
      "Calculando tasa de error de símbolo teórica\n",
      "Calculando tasa de error de bit teórica\n",
      "Representando resultados\n",
      "\n",
      "\n",
      "Simulando sistema PAM con M=8\n",
      " Simulando SNR = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, \n",
      "Calculando tasa de error de símbolo teórica\n",
      "Calculando tasa de error de bit teórica\n",
      "Representando resultados\n",
      "\n",
      "\n",
      "Simulando sistema PAM con M=16\n",
      " Simulando SNR = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, \n",
      "Calculando tasa de error de símbolo teórica\n",
      "Calculando tasa de error de bit teórica\n",
      "Representando resultados\n",
      "\n",
      "\n",
      "Simulando sistema PAM con M=32\n",
      " Simulando SNR = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, \n",
      "Calculando tasa de error de símbolo teórica\n",
      "Calculando tasa de error de bit teórica\n",
      "Representando resultados\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#%% Calculamos para cada M la estimación mediante simulación de la probabilidad\n",
    "# de error de símbolo y de bit equivalente\n",
    "for M in np.array([2, 4, 8, 16, 32]):  \n",
    "    if verbose: print('\\n\\nSimulando sistema PAM con M={}'.format(M))\n",
    "    jj = jj+1;\n",
    "    Bn = np.random.randint(2,size=Nb)   \n",
    "    \n",
    "    [Xn,Bn,An,phi,alfabeto] = transmisorpam(Bn,Eb,M,p,L)\n",
    "    #[Secuencia Tx, Bits Tx, Símbolos Tx, pulso usado, constelación]\n",
    "\n",
    "    Nb = len(Bn) #Número de bits transmitidos\n",
    "    Ns = len(An) #Número de símbolos transmitidos\n",
    "\n",
    "    # Definamos la relación señal ruido en dB y otros elementos \n",
    "    SNRdb = np.arange(20)\n",
    "    serSimulada = np.zeros(len(SNRdb))\n",
    "    berSimulada = np.zeros(len(SNRdb))\n",
    "    if verbose: print(' Simulando SNR = ', end = '')\n",
    "    for ii in range(len(SNRdb)):\n",
    "        if verbose: print('{}, '.format(SNRdb[ii]), end = '')\n",
    "        SNR = 10**(SNRdb[ii]/10)\n",
    "        varzn = Eb/(2*SNR)\n",
    "    \n",
    "        # El ruido (AWGN) \n",
    "        Wn = np.sqrt(varzn)*np.random.randn(np.size(Xn))\n",
    "        \n",
    "        # Salida del Canal AWGN: la señal Tx más el ruido\n",
    "        rn = Xn + Wn\n",
    "    \n",
    "        # Salida del Receptor Vectorial o Demodulador, usando correlador\n",
    "        r = np.dot(np.reshape(rn,[Ns,L]), phi)\n",
    "\n",
    "        # Salida del Detector \n",
    "        Andetectado = detectaSBF(r, alfabeto) #Símbolos detectados\n",
    "        erroresSimbolo = Ns-sum(Andetectado==An) # Nº Errores de símbolo\n",
    "        serSimulada[ii] = erroresSimbolo/Ns  \n",
    "        \n",
    "        Bndetectado = simbolobit(Andetectado, alfabeto) #Bits detectados\n",
    "        erroresBit = Nb-sum(Bndetectado==Bn) # Nº Errores de bit\n",
    "        berSimulada[ii] = erroresBit/Nb\n",
    "  \n",
    "    # Cálculo de la probabilidad o tasa de error teórico\n",
    "    SNRdb2 = np.arange(20, step=0.01) #SNR donde calcular la BER teórica\n",
    "    SNR2 = 10**(SNRdb2/10) #paso a unidades naturales\n",
    "    if verbose: print('\\nCalculando tasa de error de símbolo teórica')      \n",
    "    serTeorico = (2*(M-1)/M)*Qfunct(np.sqrt((6*np.log2(M)/(M**2-1))*SNR2))\n",
    "    if verbose: print('Calculando tasa de error de bit teórica') \n",
    "    berTeorico = serTeorico/np.log2(M)\n",
    "\n",
    "    # Representación de las tasas de error de símbolo y bit, teórica y simuladas\n",
    "    if verbose: print('Representando resultados')  \n",
    "    color = codigocolor[jj,:]  \n",
    "    plt.figure(73,figsize=(10,6))  # SER \n",
    "    plt.semilogy(SNRdb,serSimulada,'r*')\n",
    "    plt.semilogy(SNRdb2,serTeorico,'-k',color=color, linewidth=2)\n",
    "    plt.axis([0,20,10**-7,1])\n",
    "    plt.grid()\n",
    "    plt.xlabel('Relación Señal/Ruido $E_b/N_0$ (dB)');\n",
    "    plt.ylabel('Probabilidad de error de símbolo ($P_M$)');\n",
    "    plt.figure(74,figsize=(10,6)) # BER \n",
    "    plt.semilogy(SNRdb,berSimulada,'r*')\n",
    "    plt.semilogy(SNRdb2,berTeorico,'-k',color=color, linewidth = 1)\n",
    "    plt.axis([0,20,10**-7,1])\n",
    "    plt.grid()\n",
    "    plt.xlabel('Relación Señal/Ruido $E_b/N_0$ (dB)')\n",
    "    plt.ylabel('Probabilidad de error de bit equivalente ($P_b$)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejercicio propuesto (básico)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basándose en el sistema de comunicación PAM banda base diseñado en la Sección 7.2, en este Ejercicio Básico se desea diseñar un sistema de comunicación PAM paso de banda M-ario en un canal AWGN y comprobar sus prestaciones para diversos valores de M y relación señal/ruido. En concreto:\n",
    "\n",
    "**1.** Diseñar un transmisor paso de banda mediante la función transmisorpam, cuando: \n",
    "\n",
    " - $N_b = 10^6$ bits  \n",
    " - $M = 8$.\n",
    " - $E_b = 8$ Julios.\n",
    " - $L = 32$.\n",
    " - El pulso conformador se muestra en la Figura 7.7 (ver a contiuación).\n",
    " - Frecuencia portadora $\\omega_c = 8\\pi/T$.\n",
    " - $R_b = 10^4$ bps.\n",
    "\n",
    "<img src=https://raw.githubusercontent.com/gapsc-us/labcomdig/main/figures/pulsoConformador.png style=\"max-width:30%;width:auto;height:auto;\" align=\"center\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.** Representar la señal discreta transmitida para los primeros 10 símbolos generados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.** Representar la señal continua transmitida para los primeros 10 símbolos generados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4.** Determinar la energía de la señal transmitida. ¿Coincide la energía media por bit transmitida con la elegida en el diseño? Razonar la posible discrepancia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5.** Demodular la señal transmitida cuando se utiliza un demodulador que utiliza un único correlador, en ausencia de ruido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6.** Representar la respuesta impulsiva discreta de un filtro adaptado que pudiera utilizarse como parte de un demodulador. Demodular la señal transmitida cuando se utiliza un demodulador que utiliza el filtro adaptado, en ausencia de ruido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7.** Determinar los posibles niveles a la entrada del detector, en ausencia de ruido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8.** Determinar la varianza del ruido presente a la entrada del detector cuando la relación Ebav/N0 = 16 dB. ¿Cuáles serían los umbrales de detección MAP?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**9.** La probabilidad de error de símbolo PM y la probabilidad de error de bit equivalente Pb pueden obtenerse a partir de las ecuaciones (7.1) y (7.2). Representarlas frente a Ebav/N0 medido en decibelios cuando Ebav/N0 varía entre 0 y 20 dB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**10.** Utilizando una secuencia de Nb = $10^6$ bits transmitidos, comprobar experimentalmente que los valores de Pb obtenidos concuerdan con los teóricos cuando la relación Ebav/N0 varía entre 0 y 20 dB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejercicio propuesto\n",
    "\n",
    "Basándose en el receptor PAM diseñado, en este ejercicio se desea diseñar un sistema 32-QAM rectangular en banda base y obtener experimentalmente la probabilidad de error de bit equivalente de la misma. Debido a su posible complejidad, se propone un guión de cómo se podría realizar este diseño.\n",
    "\n",
    "**1.** Diseñar un transmisor paso de banda mediante la función transmisorpam, cuando:\n",
    " - $N_b = 10^6$\n",
    " - $M = 8$\n",
    " - $E_b = 8$ Julios.\n",
    " - $L = 32$.\n",
    " - El pulso conformador se muestra en la Figura 7.7 (ver abajo).\n",
    " - Frecuencia portadora $\\omega_c = 8\\pi/T$.\n",
    " - $R_b = 10^4$ bps.\n",
    " \n",
    " <img src=https://raw.githubusercontent.com/gapsc-us/labcomdig/main/figures/pulsoConformador.png style=\"max-width:30%;width:auto;height:auto;\" align=\"center\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.** El receptor estará formado por dos receptores PAM diseñados mediante correladores. Asuma que no hay canal, esto es que el transmisor y receptor están conectados directamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.** Si llamamos {ÂnI} a la secuencia de símbolos detectados en la componente en fase y {ÂnQ} a los detectados en la componente en cuadratura, hacer uso de la función simbolobit para obtener las correspondientes secuencias de bits de las componentes en fase y cuadratura, {B̂nI} y {B̂nQ}, respectivamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4.** La secuencia de bit detectada debe obtenerse intercalando las secuencias de bit anteriores. Esta operación puede realizarse con facilidad mediante el uso de la función que se ha denominado une, que realiza la operación simétrica a la función split. Una posible realización de esta función une se muestra en el siguiente código.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def une(BndetectadoI,BndetectadoQ,M1,M2):\n",
    "    '''\n",
    "    [Bndetectado]=une(BndetectadoI,BndetectadoQ,M1,M2)\n",
    "    BndetectadoI = una secuencia de símbolos binarios correspondientes a los\n",
    "    bits en posiciones múltiplos de k1\n",
    "    BndetectadoQ = una secuencia de símbolos binarios correspondientes a los\n",
    "    bits en posiciones múltiplos de k2\n",
    "    M1 = nº de símbolos de la componente en fase\n",
    "    M2 = nº de símbolos de la componente en cuadratura\n",
    "    '''\n",
    "    # Devuelve\n",
    "    # Bndetectado = Los bits entremezclados\n",
    "    import numpy as np\n",
    "    k1 = np.log2(M1)\n",
    "    k2 = np.log2(M2)\n",
    "    N = len(BndetectadoI)/k1\n",
    "    C1 = np.reshape(BndetectadoI,[int(N),int(k1)])\n",
    "    C2 = np.reshape(BndetectadoQ,[int(N),int(k2)])\n",
    "    C = np.c_[C1,C2]\n",
    "    Bndetectado = np.reshape(C,[int(N*(k1+k2))])\n",
    "    return Bndetectado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5.** Obtenga mediante simulación, experimentalmente, cuál es la tasa de error de bit, BER, si se introduce en el sistema anterior un canal AWGN. Estime la BER para un rango entre 0 y 20 dB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**6.** La probabilidad de error de bit equivalente del sistema QAM se obtendrá comparando la secuencia de dígitos binarios transmitidos y los detectados. A efectos de comparación, se sabe que esta probabilidad de error viene dada por:\n",
    "\n",
    "$$P_b = \\frac{N_e}{log_2 M_1 + log_2 M_2} Q\\left[\\sqrt{\\frac{6(log_2 M_1 + log_2 M_2}{M_1^2 + M_2^2 -2}\\frac{E_{bav}}{N_0}}\\right]$$\n",
    "\n",
    "siendo Ne el número medio de puntos que comparten frontera, que en el caso de una constelación rectangular QAM viene dado por:\n",
    "\n",
    "$$ N_e = 4\\left(1 - \\frac{1}{2M_1} - \\frac{1}{2M_2}\\right)$$\n",
    "\n",
    "Compare el resultado experimental y teórico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**7.** Repetir todos los puntos anteriores utilizando únicamente la función transmisorpam para generar la señal QAM.\n",
    "**8.** Repetir todos los puntos anteriores utilizando la función transmisorqamVC diseñada en el Ejercicio 5.7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**8.** Repetir todos los puntos anteriores utilizando la función transmisorqamVC diseñada en el Ejercicio 5.7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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