Untitled8.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyN1Od1PMIbxergE22t7kVWw",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/iTosy/Scipy_Bpsk_code_for_SNR-4db/blob/main/Untitled8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "jV2ol3HE_sZf",
        "outputId": "00b1ab2c-e48c-4284-8649-ef24567fcbba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.04923333333333333\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Eb/N0 Vs BER for BPSK over AWGN (complex baseband model)\n",
        "# © Author: Mathuranathan Viswanathan (gaussianwaves.com)\n",
        "import numpy as np #for numerical computing\n",
        "import matplotlib.pyplot as plt #for plotting functions\n",
        "from scipy.special import erfc #erfc/Q function\n",
        "\n",
        "#---------Input Fields------------------------\n",
        "nSym = 120000 # Number of symbols to transmit\n",
        "EbN0dB = 4\n",
        "\n",
        "\n",
        "M=2 #Number of points in BPSK constellation\n",
        "m = np.arange(0,M) #all possible input symbols\n",
        "A = 1; #amplitude\n",
        "constellation = A*np.cos(m/M*2*np.pi)  #reference constellation for BPSK\n",
        "\n",
        "#------------ Transmitter---------------\n",
        "inputSyms = np.random.randint(low=0, high = M, size=nSym) #Random 1's and 0's as input to BPSK modulator\n",
        "s = constellation[inputSyms] #modulated symbols\n",
        "\n",
        "fig, ax1 = plt.subplots(nrows=1,ncols = 1)\n",
        "ax1.plot(np.real(constellation),np.imag(constellation),'*')\n",
        "\n",
        "#----------- Channel --------------\n",
        "#Compute power in modulatedSyms and add AWGN noise for given SNRs\n",
        "\n",
        "gamma = 10**(EbN0dB/10) #SNRs to linear scale\n",
        "P=sum(abs(s)**2)/len(s) #Actual power in the vector\n",
        "N0=P/gamma # Find the noise spectral density\n",
        "n = np.sqrt(N0/2)*np.random.standard_normal(s.shape) # computed noise vector\n",
        "r = s + n # received signal\n",
        "    \n",
        "#-------------- Receiver ------------\n",
        "detectedSyms = (r <= 0).astype(int) #thresolding at value 0\n",
        "i=0\n",
        "f=0\n",
        "su=0\n",
        "for k in range(nSym):\n",
        "  if i==4:\n",
        "    if f==1:\n",
        "      su+=1\n",
        "    i=0\n",
        "    f=0\n",
        "  if detectedSyms[k]!=inputSyms[k]:\n",
        "    f=1\n",
        "  i+=1\n",
        "BLER=su/(nSym/4)\n",
        "print(BLER)"
      ]
    }
  ]
}