Tema5.Transmisor_Lineal_II_(PSK_y_QAM).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 5. Modulador Lineal II, PSK y QAM\n",
    "\n",
    "**Este notebook contiene código del Tema 5**"
   ]
  },
  {
   "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/Tema5.Transmisor_Lineal_II_(PSK_y_QAM).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": [
    "## Modulación M-PSK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diseño experimental de un transmisor M-PSK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Código 5.1** Transmisor M-PSK.\n",
    "\n",
    "Las funciones que se describen aquí están en el fichero labcomdig.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transmisorpsk(Bn,Eb,M,p1,p2,L): \n",
    "   '''\n",
    "   [Xn,Bn,An,phi1,phi2,alfabeto] = transmisorpsk(Bn,Eb,M,p1,p2,L)\n",
    "   \n",
    "   Entradas:\n",
    "      Bn = Secuencia de dígitos binarios\n",
    "      Eb = Energía media por bit transmitida en Julios\n",
    "      M = Número de símbolos del código PSK\n",
    "      p1 = Pulso real de la componente en fase\n",
    "      p2 = Pulso real de la componente en cuadratura\n",
    "      L = Número de puntos que se utilizará 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 símbolos complejos transmitidos\n",
    "      phi1 = Pulso básico real normalizado (energí­a unidad) de la componente en fase\n",
    "      phi2 = Pulso básico real normalizado (energí­a unidad) de la componente en cuadratura\n",
    "      alfabeto = El alfabeto utilizado asociado a cada símbolo transmitido\n",
    "   '''\n",
    "   \n",
    "   #Comprobación de parámetros de entrada\n",
    "   p1 = p1.squeeze()\n",
    "   p2 = p2.squeeze()\n",
    "   if len(Bn)<1 or Eb<=0 or M<2 or p1@p1==0 or p2@p2==0 or L<1:\n",
    "      raise Exception('Error: revise los parámetros de entrada')\n",
    "        \n",
    "   #Definiciones\n",
    "   eps = np.finfo(float).eps # por debajo de este valor se considera cero\n",
    "    \n",
    "   # Comprobación de pulsos\n",
    "   Ls1 = len(p1)\n",
    "   Ls2 = len(p2) \n",
    "   if Ls1<L:\n",
    "      p1 = np.r_[p1, np.zeros(int(L-Ls1))]\n",
    "   elif Ls1>L:\n",
    "      p1 = p1[:L]\n",
    "      print('La duración del pulso se trunca a {} muestras'.format(str(L)))\n",
    "   if Ls2<L:\n",
    "      p2 = np.r_[p2, np.zeros(int(L-Ls2))]\n",
    "   elif Ls2>L:\n",
    "      p2 = p2[:L]\n",
    "      print('La duración del pulso se trunca a {} muestras'.format(str(L)))       \n",
    "       \n",
    "   # Se comprueba la ortogonalidad\n",
    "   if abs(p1@p2) >= 1e2*eps:\n",
    "      raise Exception('No es posible realizar la transmisión')  \n",
    "       \n",
    "   # Se normalizan las energías de los pulsos\n",
    "   phi1 = p1 / np.sqrt(p1@p1) \n",
    "   phi2 = p2 / np.sqrt(p2@p2)  \n",
    "   # Se genera el pulso complejo [Ver la ecuación (5.25)]\n",
    "   phi = phi1 - 1j*phi2\n",
    "   \n",
    "   # Obtención de los niveles asociados a cada símbolo: alfabeto\n",
    "   k = int(np.ceil(np.log2(M))) # Número de bits por símbolo\n",
    "   M = 2**(k) # Se ajusta M a una potencia de dos\n",
    "   \n",
    "   # El alfabeto [Ver la ecuación (5.24)]\n",
    "   alfabeto = np.sqrt(Eb*k)*np.exp(1j*2*np.pi*np.arange(M)/M) #empieza en 0\n",
    "   \n",
    "   # Si la longitud de Bn no es múltiplo de k, se completa con ceros\n",
    "   Nb = len(Bn) \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",
    "   \n",
    "   # Número de bits y símbolos que vamos a transmitir\n",
    "   Nb = len(Bn) # Número de bits que vamos a transmitir tras la corrección \n",
    "   Ns = int(Nb/k)\n",
    "\n",
    "   # La secuencia generada\n",
    "   if M>2:\n",
    "      An = alfabeto[gray2de(np.reshape(Bn,[Ns,k]))]\n",
    "   else:\n",
    "       An = alfabeto[Bn]\n",
    "    \n",
    "   # Obtención del tren de pulsos,    Xn = np.real(np.kron(An, phi))  ó\n",
    "   Xn = np.real(np.reshape(np.reshape(An,[Ns,1])*phi,[Ns*L,]))\n",
    "   return [Xn, Bn, An, phi1, phi2, alfabeto]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se necesita la siguiente función, del notebook 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": [
    "**Código 5.2** Generación de una señal M-PSK paso de baja utilizando la función transmisorpsk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Parámetros suministrados:\n",
    "#Bn = Secuencia de dígitos binarios\n",
    "Bn = np.array([0,0,0,0,0,1,0,1,1,0,1,0,1,1,0,1,1,1,1,0,1,1,0,0])\n",
    "Eb = 4  # Eb = Energia media transmitida en Julios\n",
    "M = 8  # M = número de símbolos\n",
    "Rb = 10**3 # Rb = Tasa de bit de la señal\n",
    "L = 8 # L = número de puntos que vamos a utilizar para transmitir un sí­mbolo\n",
    "\n",
    "#Definición de los pulsos de las componentes en fase y cuadratura\n",
    "p1 = np.r_[np.ones(int(L/2)), np.zeros(int(L/2))]\n",
    "p2 = np.r_[np.zeros(int(L/2)), np.ones(int(L/2))]\n",
    "\n",
    "#Generación de la señal modulada\n",
    "[Xn, Bn, An, phi1, phi2, alfabeto] = transmisorpsk(Bn, Eb, M, p1, p2, L)\n",
    "\n",
    "#Parámetros que han podido ser actualizados por transmisorpsk\n",
    "L = len(phi1) #Número de muestras por símbolo\n",
    "Nb = len(Bn) #Número de bits transmitidos\n",
    "M = len(alfabeto) #Número de símbolos de la constelación\n",
    "\n",
    "# Representación en tiempo discreto\n",
    "plt.figure(51,figsize=(16, 6))\n",
    "plt.stem(Xn,linefmt='b-',markerfmt='bo',basefmt='b-',use_line_collection = True)\n",
    "plt.xlabel('n'); plt.ylabel('X(n)')\n",
    "plt.grid(), plt.show()\n",
    "\n",
    "#Definición de los parámetros del tiempo continuo para representar\n",
    "Tb = 1/Rb            #Duración del bit en segundos\n",
    "Tm = (Tb*np.log2(M))/L  #Intervalo entre cada muestra de la señal\n",
    "Td = Tb*Nb         #Duración total de la señal\n",
    "t = np.arange(0,Td,Tm)    #Eje de representación de la señal en tiempo continuo\n",
    "Xt = np.sqrt(1/Tm)*Xn\n",
    "\n",
    "# Representación en tiempo continuo\n",
    "plt.figure(52,figsize=(16, 6)); plt.plot(t,Xt, linewidth=2.0, COLOR='b')\n",
    "plt.xlabel('t[s]'), plt.ylabel('X(t)')\n",
    "plt.grid(), plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Código 5.3** Generación de una señal M-PSK paso de banda utilizando la función transmisorpsk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Parámetros suministrados:\n",
    "#Bn = Secuencia de dígitos binarios\n",
    "Bn = np.array([0,0,0,1,1,1,1,0,0,1,1,0,1,1,0,1,1,1,0,0])\n",
    "Eb = 4 #Eb = Energia media transmitida en Julios\n",
    "M = 4 # M = número de símbolos\n",
    "Rb = 10**3 # Rb = Tasa de bit de la señal\n",
    "L = 32 #L = número de puntos que vamos a utilizar para transmitir un sí­mbolo\n",
    "\n",
    "#Definición de los pulsos de las componentes en fase y cuadratura\n",
    "p1 = np.cos(2*np.pi*np.arange(L)/(L/2))\n",
    "p2 = np.sin(2*np.pi*np.arange(L)/(L/2))\n",
    "\n",
    "# Se genera la señal modulada\n",
    "[Xn, Bn, An, phi1, phi2, alfabeto] = transmisorpsk(Bn, Eb, M, p1, p2, L)\n",
    "\n",
    "#Parámetros que han podido ser actualizados por transmisorpsk\n",
    "L = len(phi1)    #Número de muestras por símbolo\n",
    "Nb = len(Bn)    #Número de bits transmitidos\n",
    "M = len(alfabeto)   #Número de símbolos de la constelación\n",
    "\n",
    "# Representación en tiempo discreto\n",
    "plt.figure(53,figsize=(16, 6))\n",
    "plt.stem(Xn,linefmt='b-',markerfmt='bo',basefmt='b-',use_line_collection = True)\n",
    "plt.xlabel('n'); plt.ylabel('X(n)')\n",
    "plt.grid(), plt.show()\n",
    "\n",
    "#Definición de los parámetros del tiempo continuo para representar\n",
    "Tb = 1/Rb                #Duración del bit en segundos\n",
    "Tm = (Tb*np.log2(M))/L      #Intervalo entre cada muestra de la señal\n",
    "Td = Tb*Nb             #Duración total de la señal\n",
    "t = np.arange(0,Td,Tm)*1e3    #Eje de representación de la señal en tiempo continuo\n",
    "Xt = np.sqrt(1/Tm)*Xn\n",
    "\n",
    "# Representación en tiempo continuo\n",
    "plt.figure(54,figsize=(16, 6)), plt.plot(t,Xt, linewidth=2.0,color='b')\n",
    "plt.xlabel('t [ms]'), plt.ylabel('X(t)')\n",
    "plt.grid(), plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transmisor QAM rectangular: diseño experimental"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Código 5.4** Obtención de las secuencias en fase y cuadratura"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split(Bn, M1, M2):\n",
    "    '''\n",
    "    Entradas\n",
    "        Bn = una secuencia de símbolos binarios\n",
    "        M1 = nº de símbolos de la componente en fase\n",
    "        M2 = nº de símbolos de la componente en cuadratura    \n",
    "    Salida     \n",
    "        Bn = una secuencia de símbolos binarios\n",
    "        M1 = nº de símbolos de la componente en fase\n",
    "        M2 = nº de símbolos de la componente en cuadratura\n",
    "    '''\n",
    "    \n",
    "    k1 = int(np.log2(M1))\n",
    "    k2 = int(np.log2(M2))\n",
    "    k = k1 + k2\n",
    "    \n",
    "    #Longitud de la secuencia\n",
    "    Nb = len(Bn)\n",
    "    \n",
    "    #Una matriz con Ns=Nb/k filas formadas por los k bits\n",
    "    W = np.reshape(Bn,[int(Nb/k),k])\n",
    "    \n",
    "    #Extrae la submatriz formada por los k1 primeros bits y pone una fila tras otra\n",
    "    BnI = np.reshape(W[:,:k1],[k1*int(Nb/k)])\n",
    "    \n",
    "    #Extrae la submatriz formada por los k2 bits restantes y pone una fila tras otra\n",
    "    BnQ = np.reshape(W[:,k1:],[k2*int(Nb/k)]) \n",
    "    \n",
    "    return BnI, BnQ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejemplos de uso de la función transmisorpam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Código 5.5** Transmisor QAM rectangular"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transmisorqam(Bn, Eb, M1, M2, p1, p2, L): \n",
    "    '''\n",
    "    [Xn,BnI,BnQ,AnI,AnQ,AI,AQ,phi1,phi2] = transmisorqam(Bn, Eb, M1, M2,p1,p2,L)\n",
    "    \n",
    "    Entradas:\n",
    "     Bn = Secuencia de dígitos binarios\n",
    "     Eb = Energía media por bit transmitida en Julios\n",
    "     M1 = Nº de símbolos de la componente en fase\n",
    "     M2 = Nº de símbolos de la componente en cuadratura\n",
    "     p1 = Pulso real de la componente en fase\n",
    "     p2 = Pulso real de la componente en cuadratura\n",
    "     L = Nº de puntos que vamos a utilizar en la representación un símbolo\n",
    "        \n",
    "    Devuelve:\n",
    "     Xn = la señal de información digital\n",
    "     BnI = Bits transmitidos por la componente en fase\n",
    "     BnQ = Bits transmitidos por la componente en cuadratura\n",
    "     AnI = Niveles de amplitud transmitidos por la componente en fase\n",
    "     AnQ = Niveles de amplitud transmitidos por la componente en cuadratura \n",
    "     AI = Niveles de amplitud usados en la componente en fase\n",
    "     AQ = Niveles de amplitud usados en la componente en cuadratura\n",
    "     phi1 = Pulso básico normalizado (energí­a unidad) de la componente en fase\n",
    "     phi2 = Pulso básico normalizado (energí­a unidad) de la componente en cuadratura \n",
    "    '''\n",
    "    #Comprobación de parámetros de entrada\n",
    "    if len(Bn)<1 or Eb<=0 or M1<2 or M2<2  or p1@p1==0 or p2@p2==0 or L<1:\n",
    "       raise Exception('Error: revise los parámetros de entrada')    \n",
    "       \n",
    "    #Definiciones\n",
    "    eps = np.finfo(float).eps # por debajo de este valor se considera cero\n",
    "    \n",
    "    #Comprobación de los pulsos básicos\n",
    "    Ls1 = len(p1)\n",
    "    Ls2 = len(p2)\n",
    "    if Ls1<L:\n",
    "        p1 = np.r_[p1, np.zeros(int(L-Ls1))]\n",
    "    elif Ls1>L:\n",
    "        p1 = p1[:L]\n",
    "        print('La duración del pulso se trunca a {} muestras'.format(str(L)))\n",
    "    if Ls2<L:\n",
    "        p2 = np.r_[p2, np.zeros(int(L-Ls2))]\n",
    "    elif Ls1>L:\n",
    "        p2 = p2[:L]\n",
    "        print('La duración del pulso se trunca a {} muestras'.format(str(L)))       \n",
    "\n",
    "    #Normalicemos las energías de los pulsos\n",
    "    phi1 = 1/np.sqrt(p1@p1)*p1\n",
    "    phi2 = 1/np.sqrt(p2@p2)*p2\n",
    "    \n",
    "    #Comprobemos la ortogonalidad\n",
    "    if np.abs(phi1@phi2) >= 1e2*eps:\n",
    "        raise Exception('Bases no ortogonales, no es posible realizar la transmisión')\n",
    "    \n",
    "    #Ajuste de los parámetros   \n",
    "    k1 = int(np.ceil(np.log2(M1))) #Número de bits de la componente en fase\n",
    "    M1 = 2**(k1) #Valor de M1 tras la corrección\n",
    "    k2 = int(np.ceil(np.log2(M2))) #Número de bist de la componente en cuadratura M2 = 2**(k2) #Valor de M2 tras la corrección\n",
    "    k = k1 + k2 #Número de bits en cada símbolo QAM\n",
    "    Nb = len(Bn); 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",
    "    \n",
    "    #Obtención de la mitad de la distancia mínima entre símbolos para Eb dada\n",
    "    A = np.sqrt(3*Eb*np.log2(M1*M2)/(M1**2+M2**2-2))\n",
    "    \n",
    "    #El alfabeto con los niveles\n",
    "    AI = A * (2*np.arange(M1)-M1+1)\n",
    "    AQ = A * (2*np.arange(M2)-M2+1)\n",
    "    \n",
    "    #Separación de la secuencia de bits en las secuencias de las componentes en fase y cuadratura\n",
    "    BnI,BnQ = split(Bn,M1,M2)\n",
    "    NbI = len(BnI)\n",
    "    NbQ = len(BnQ)\n",
    "    \n",
    "    #Obtención de la secuencia de símbolos de las componentes en fase y cuadratura\n",
    "    if M1>2:\n",
    "        AnI = AI[gray2de(np.reshape(BnI,[int(NbI/k1),k1]))] \n",
    "    else:\n",
    "        AnI = AI[BnI]\n",
    "    if M2>2:\n",
    "        AnQ = AQ[gray2de(np.reshape(BnQ,[int(NbQ/k2),k2]))]\n",
    "    else:\n",
    "        AnQ = AQ[BnQ]\n",
    "    Ns = len(AnI)    \n",
    "    \n",
    "    #Las componentes en fase, cuadratura y total de la señal discreta a transmitir\n",
    "    XnI=np.reshape(np.reshape(AnI,[Ns,1])*phi1,[Ns*L,]) #ó XnI=np.kron(AnI, phi1);\n",
    "    XnQ=np.reshape(np.reshape(AnQ,[Ns,1])*phi2,[Ns*L,]) #ó XnQ=np.kron(AnQ, phi2);\n",
    "    Xn = XnI+XnQ;\n",
    "    return Xn, BnI, BnQ, AnI, AnQ, AI, AQ, phi1, phi2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Código 5.6** Generación de una señal QAM rectangular paso de baja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Parámetros suministrados:\n",
    "#Bn = Secuencia de dígitos binarios\n",
    "Bn = np.random.randint(2,size=[64])\n",
    "Eb = 4 # Eb = Energia media transmitida en Julios\n",
    "M1 = 4 # M1 = Nº de símbolos de la componente en fase\n",
    "M2 = 2 # M2 = Nº de símbolos de la componente en cuadratura\n",
    "Rb = 10**3 # Rb = Tasa de bit de la señal\n",
    "L = 32 # L = número de puntos que vamos a utilizar para transmitir un sí­mbolo\n",
    "\n",
    "#Definición de los pulsos de las componentes en fase y cuadratura\n",
    "p1 = np.r_[np.ones(int(L/2)), np.zeros(int(L/2))] \n",
    "p2 = np.r_[np.zeros(int(L/2)), np.ones(int(L/2))]\n",
    "\n",
    "# Señal modulada QAM\n",
    "[Xn,BnI,BnQ,AnI,AnQ,AI,AQ,phi1,phi2] = transmisorqam(Bn, Eb, M1, M2,p1, p2, L);\n",
    "\n",
    "#Parámetros que han podido ser actualizados por transmisorpsk\n",
    "L = len(phi1)          #Número de muestras por símbolo\n",
    "Nb = len(BnI)+len(BnQ) #Número de bits transmitidos\n",
    "M = len(AI)*len(AQ)\t   #Número de símbolos de la constelación\n",
    "\n",
    "# Representación en tiempo discreto\n",
    "plt.figure(55,figsize=(16, 6))\n",
    "plt.stem(Xn,linefmt='b-',markerfmt='bo',basefmt='b-', use_line_collection = True)\n",
    "plt.xlabel('n'); plt.ylabel('X(n)')\n",
    "plt.grid(), plt.show()\n",
    "\n",
    "#Definición de los parámetros del tiempo continuo para representar\n",
    "Tb = 1/Rb                #Duración del bit en segundos\n",
    "Tm = (Tb*np.log2(M))/L      #Intervalo entre cada muestra de la señal\n",
    "Td = Tb*Nb             #Duración de la señal\n",
    "t = np.arange(0,Td,Tm)         #Eje de representación de la señal en tiempo continuo\n",
    "Xt = np.sqrt(1/Tm) * Xn\n",
    "\n",
    "# Representación en tiempo continuo\n",
    "plt.figure(56,figsize=(16, 6)), plt.plot(t,Xt, linewidth=2.0,color='b')\n",
    "plt.xlabel('t[s]'), plt.ylabel('X(t)')\n",
    "plt.grid(), plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejercicio propuesto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1.** Dada una secuencia de bit {0,0,0,1,1,0,1,1} se desea transmitir mediante un transmisor QPSK paso de banda. Esto es, un transmisor 4-PSK paso de banda en el que las fases de las señales transmitidas sean {$\\frac{\\pi}{4}$, $\\frac{3\\pi}{4}$, $\\frac{5\\pi}{4}$, $\\frac{7\\pi}{4}$}. El pulso conformador es rectangular, utilizando L = 64 muestras para transmitir cada símbolo M-ario, la tasa de transmisión es $R_b = 10^3$ bps, la energía por bit transmitida es igual a $E_b$ = 4 J y la frecuencia portadora viene dada por $\\omega_c = 8\\pi/T$. Una manera de realizar este diseño consiste en modificar la función transmisorpsk propuesta en la memoria de la práctica para que admita un offset de las fases transmitidas. Representar la señal a la salida del transmisor tanto en forma discreta como continua."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2.** Diseñar un transmisor QAM rectangular paso de banda con $M_1$ = $M_2$ = 2. El pulso conformador es rectangular, la tasa de transmisión es $R_b = 10^3$ bps, la energía por bit transmitida es igual a $E_b = 4$ J y la frecuencia portadora viene dada por $\\omega_c = 8\\pi/T$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3.** Utilizar los dos diseños anteriores para realizar una transmisión con $R_b = 10^4$ bps y una energía por bit $E_b = 4$ J cuando el pulso conformador $g(t)$ es el mostrado en la Figura 5.20. La frecuencia portadora viene dada por $\\omega_c = 8\\pi/T$.\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": []
  }
 ],
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