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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 7: Optical receiver operation"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 7.1, Page Number: 258"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "\n",
      "#variable declaration\n",
      "bon = 1.0                                            #signal on level\n",
      "boff = 0.0                                           #signal off level\n",
      "sigma_on = 1.0                                       #variance sigma-on level\n",
      "sigma_off = 1.0                                      #variance sigma-on level\n",
      "\n",
      "#calculation\n",
      "Q = (bon - boff)/(sigma_on + sigma_off)              #parameter value\n",
      "Vth = bon-Q*sigma_on                                 #optimum decision threshold\n",
      "\n",
      "#result\n",
      "print \"Q parameter value = \" ,Q\n",
      "print \"Optimum decision threshold Vth = \",Vth"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Q parameter value =  0.5\n",
        "Optimum decision threshold Vth =  0.5\n"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 7.2, Page Number: 258"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "import pylab\n",
      "import numpy as np\n",
      "import scipy as sp\n",
      "from scipy import special\n",
      "\n",
      "#variable declaration\n",
      "Q = 6.0                                              #parameter value from figure\n",
      "\n",
      "#calculation\n",
      "Pe = (1.0/2.0)*(1-math.erf(Q/math.sqrt(2.0)))        #probability of error \n",
      "S_N_dB = 10*math.log10(2*Q)                          #signal to naoise ratio (dB)\n",
      "\n",
      "#result\n",
      "print \"Probability of error = \" ,round(Pe,10)\n",
      "print \"Signal to naoise ratio =\",round(S_N_dB,1),\"dB\"\n",
      "\n",
      "#plot\n",
      "q1=arange(0.0, 8.0, 0.5)\n",
      "Pe1 = (1.0/2.0)*(1-sp.special.erf(q1/sqrt(2.0))) \n",
      "plot(-q1+8,-Pe1)\n",
      "ylabel('BER (Pe)')\n",
      "xlabel('factor Q')\n",
      "title('Plot of BER (Pe) versus the factor Q')\n",
      "text(6,-0.3,'Q=5.99 for')\n",
      "text(6,-0.35,' Pe=10^-9')    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Probability of error =  1e-09\n",
        "Signal to naoise ratio = 10.8 dB\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x12d58c88>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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Ig9exI0yZEqwQK9G1JJYAo9y93Mw6ACXu3itJ2SLgUbUkRKS+rF4NvXvD/Pmw++5RR1N3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\niIgkpSQhIiJJKUmIiEhS/x8iKeBNY2dz/AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa8c5e10>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 7.3, Page Number: 259"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.3(a)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "\n",
      "#variable declaration\n",
      "s_n=8.5                            #signal-to-noise ratio\n",
      "s_n_db=18.6                        #signal-to-noise ratio (dB)\n",
      "tele_rate=1.544                    #telephone rate\n",
      "v_by_sig=12.0                      #peak signal-to-rms-noise ratio\n",
      "v_by_sig_db=21.6                   #peak signal-to-rms-noise ratio(dB)\n",
      "\n",
      "#calculation\n",
      "Pe1=(1.0/math.sqrt(2.0*math.pi))*(math.exp(-((s_n**2.0)/8.0))/(s_n/2.0))    #Probability of error 1    \n",
      "q=v_by_sig/2                                                                #parameter value\n",
      "ber=(1.0/2.0)*(1.0-math.erf(q/math.sqrt(2.0)))                              #bit error rate or Probability of error 2\n",
      "\n",
      "#result\n",
      "print \"Probability of error for signal-to-noise ratio 8.5 = \", round(Pe1,5)\n",
      "print \"Probability of error for peak signal-to-rms-noise ratio 12.0 = \", round(ber,10)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Probability of error for signal-to-noise ratio 8.5 =  1e-05\n",
        "Probability of error for peak signal-to-rms-noise ratio 12.0 =  1e-09\n"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Example 7.3(b)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "\n",
      "#variable declaration\n",
      "v_by_sig=13.0                      #peak signal-to-rms-noise ratio for SONET\n",
      "v_by_sig_db=22.3                   #peak signal-to-rms-noise ratio(dB) for SONET\n",
      "\n",
      "#calculation\n",
      "q=v_by_sig /2                                       #parameter value\n",
      "ber=(1.0/(2.0*4.0))*(1.0-math.erf(q/math.sqrt(2.0)))      #bit error rate or Probability of error \n",
      "\n",
      "#result\n",
      "print \"Probability of error for peak signal-to-rms-noise ratio 13.0 = \", round(ber,11), \"OR\",round(ber/10,12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Probability of error for peak signal-to-rms-noise ratio 13.0 =  1e-11 OR 1e-12\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 7.4, Page Number: 262"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "\n",
      "#variable declaration\n",
      "h = 6.625*10**-34                   #planks constant(J*s)\n",
      "C = 3*10**8                         #freespace velocity(m/s)\n",
      "B = 10**6                           #data rate(Mb/s)\n",
      "Lambda = 850*10**-9                 #Wavelength (m)\n",
      "\n",
      "#calculation\n",
      "tuo = 2.0/B\n",
      "E = 20.7*h*C/Lambda                     #Energy of incident photon\n",
      "Pi = E/tuo                              #Minimum incident optical power(W)\n",
      "Pi_db = 10.0*(math.log10(Pi))-(-40)     #optical power level with reference 1mW = -40dBm\n",
      "\n",
      "#result\n",
      "print \"Minimum incident optical power = \",round(Pi*10**13,2),\"pW\"\n",
      "print \"Optical power level with reference 1mW = \",round(Pi_db,1),\"dBm\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Minimum incident optical power =  24.2 pW\n",
        "Optical power level with reference 1mW =  -76.2 dBm\n"
       ]
      }
     ],
     "prompt_number": 75
    }
   ],
   "metadata": {}
  }
 ]
}