{ "metadata": { "name": "", "signature": "sha256:9cbde2192d4d6413345bc42e22f07c27c74ab50c952d19ad97876c0eed323258" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Chapter 9: Analog links" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Example 9.1, Page Number: 320" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import scipy as sp\n", "from scipy import special\n", "\n", "%matplotlib inline\n", "\n", "#variable decalration\n", "ib_by_ith1=1.5 #injection current1 > 1.2\n", "ib_by_ith2=1.6 #injection current2\n", "ib_by_ith3=1.7 #injection current3\n", "f = 100.0*10**6 #frequency (hz)\n", "\n", "#calculation\n", "RIN1 = ((ib_by_ith1-1)**-3)/f\n", "RIN2 = ((ib_by_ith2-1)**-3)/f\n", "RIN3 = ((ib_by_ith3-1)**-3)/f\n", "RIN_dB1 = 20*math.log10(RIN1) #noise to signal power ratio1(dB/Hz)\n", "RIN_dB2 = 20*math.log10(RIN2) #noise to signal power ratio2(dB/Hz)\n", "RIN_dB3 = 20*math.log10(RIN3) #noise to signal power ratio3(dB/Hz)\n", "\n", "#result\n", "print \"Index guided lasers lies between \",round(RIN_dB1,0),\"dB/Hz\",round(RIN_dB2,0),\"dB/Hz\",round(RIN_dB3,1),\"dB/Hz\"\n", "\n", "#plot\n", "f1=1.0*10**8 # several hundrede MHz\n", "ib_by_ith = arange(0.0, 2.5, 0.1)\n", "RIN12 = ((ib_by_ith-1.0)**-3)/f1\n", "plot(ib_by_ith,20*log10(RIN12))\n", "ylabel('RIN(dB/Hz)')\n", "xlabel('Ib/Ith')\n", "title('Plot of relative intensity. The noise level=100MHz ')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Index guided lasers lies between -142.0 dB/Hz -147.0 dB/Hz -150.7 dB/Hz\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Example 9.3, Page Number: 323" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import math\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import scipy as sp\n", "from scipy import special\n", "\n", "%matplotlib inline\n", "\n", "#variable declaration\n", "T =300.0 #room temperature(kelvin)\n", "kB = 1.38054*10**-23 #boltzmann's constant\n", "m =0.25 #modulation index\n", "RIN_dB = -143 \n", "RIN = 10.0**( RIN_dB /10) #relative intensity(db/Hz)\n", "Pc = (10**(0/10) )*10**-3 #power coupled(dB)\n", "R = 0.6 #responsivity(A/w)\n", "Be = 10**6 #bandwidth(hz) \n", "ID = 10**-9 #dark current(nA)\n", "Req = 750 #equialent resistance(ohm) \n", "Ft = 10**(3/10) #(dB)\n", "M = 1 #multiplication factor\n", "q = 1.602*10**-19 #Charge (coulombs)\n", "\n", "#calculation\n", "p = arange(0.0, -20.0, -1.0)\n", "P = (10**(p/10) )*10**-3\n", "C_N_1 = 0.5*((m*R*P)**2) /(4* kB*T*Be*Ft/ Req ) #limit of carrier-to-noise ratio\n", "C_N_3 = 0.5* (m**2)/( RIN *Be)\n", "C_N_2 = 0.5* (m**2)*R*P /(2* q*Be)\n", "\n", "#result\n", "print \"Decrese in received optical power at 1-db drop of C/N = 1 dB \"\n", "print \"Receiver thermal noise yields a 2-dB C/N roll off per 1-dB drop in received power at low light levels\"\n", "\n", "#plot\n", "plot(p,10*log10( C_N_1 ))\n", "plot(p,10*log10( C_N_2 ))\n", "plot (p,10*log10( C_N_3-30.6*10**6)*ones(len(p)))\n", "ylim((46,70))\n", "ylabel('Carrier-to-noise ratio(dB)')\n", "xlabel('Received optical power(dBm)')\n", "title('Plot of Carrier-to-noise ratio as a function of receiver optical power level')\n", "text(-10,57,'RIN limit')\n", "text(-17,67,'Quantum noise limit')\n", "text(-17,51,'Receiver noise limit')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Decrese in received optical power at 1-db drop of C/N = 1 dB \n", "Receiver thermal noise yields a 2-dB C/N roll off per 1-dB drop in received power at low light levels\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "" ] }, { "metadata": {}, "output_type": "display_data", "png": 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C64n7j6DhW3EHuxfjvtPvcZ2+6kb4ecDtL1riWnWv4K4f5gfN/0/ctcYNuDMIF/iDEnxZ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\nHAiNG1uWF2PSiWWCib4mwGc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