{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ec5d948b",
   "metadata": {},
   "source": [
    "## Calculating BER/BLER for PDSCH Communication with LDPC\n",
    "This notebook shows how to calculate the bit error rate of PDSCH communication with LDPC channel coding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2415601e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.io\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from neoradium import Carrier, PDSCH, CdlChannel, AntennaPanel, LdpcEncoder, Grid\n",
    "from neoradium.utils import random\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "43fe9eef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Simulating end-to-end for \"16QAM\", with \"Perfect\" channel estimation, in time domain.\n",
      "EB/No(dB)  Total Bits  Bit Errors  BER(%)  Total Blocks  Block Errors  BLER(%)  time(Sec.)\n",
      "---------  ----------  ----------  ------  ------------  ------------  -------  ----------\n",
      "    8       2417280      323481     13.38         320           320     100.00      50.77\n",
      "    9       2417280       18663      0.77         320           145      45.31      52.54\n",
      "   10       2417280           0      0.00         320             0       0.00      52.12\n",
      "   11       2417280           0      0.00         320             0       0.00      53.20\n",
      "   12       2417280           0      0.00         320             0       0.00      52.58\n",
      "   13       2417280           0      0.00         320             0       0.00      53.00\n",
      "\n",
      "Simulating end-to-end for \"16QAM\", with \"LS\" channel estimation, in time domain.\n",
      "EB/No(dB)  Total Bits  Bit Errors  BER(%)  Total Blocks  Block Errors  BLER(%)  time(Sec.)\n",
      "---------  ----------  ----------  ------  ------------  ------------  -------  ----------\n",
      "    8       2417280      469554     19.42         320           320     100.00      54.17\n",
      "    9       2417280      404225     16.72         320           320     100.00      53.96\n",
      "   10       2417280      280051     11.59         320           319      99.69      53.13\n",
      "   11       2417280      133393      5.52         320           160      50.00      55.77\n",
      "   12       2417280        1518      0.06         320            12       3.75      53.92\n",
      "   13       2417280           0      0.00         320             0       0.00      56.55\n"
     ]
    }
   ],
   "source": [
    "numFrames = 4                               # Number of time-domain frames\n",
    "ebNoDbs = range(8,14)                       # Eb/No values (in dB)\n",
    "freqDomain = False                          # Set this to True to apply channel in frequency domain\n",
    "\n",
    "modulation = \"16QAM\"\n",
    "carrier = Carrier(numRbs=51, spacing=30)    # Create a carrier with 51 RBs and 30KHz subcarrier spacing\n",
    "bwp = carrier.curBwp                        # The only bandwidth part in the carrier\n",
    "\n",
    "# Create a PDSCH object\n",
    "pdsch = PDSCH(bwp, interleavingBundleSize=0, numLayers=2, nID=carrier.cellId, modulation=modulation)\n",
    "pdsch.setDMRS(prgSize=0, configType=2, additionalPos=2)     # Specify the DMRS configuration\n",
    "\n",
    "# Create the LDPC encoder\n",
    "codeRate = 490/1024\n",
    "ldpcEncoder = LdpcEncoder(baseGraphNo=1, modulation=pdsch.modems[0].modulation, \n",
    "                          txLayers=pdsch.numLayers, targetRate=codeRate)\n",
    "ldpcDecoder = ldpcEncoder.getDecoder()\n",
    "\n",
    "numSlots = bwp.slotsPerFrame*numFrames\n",
    "results = {}\n",
    "for chanEstMethod in [\"Perfect\", \"LS\"]:                 # Two channel estimation methods\n",
    "    results[chanEstMethod] = {}\n",
    "    print(\"\\nSimulating end-to-end for \\\"%s\\\", with \\\"%s\\\" channel estimation, in %s domain.\"%\n",
    "          (modulation, chanEstMethod, \"frequency\" if freqDomain else \"time\"))\n",
    "    print(\"EB/No(dB)  Total Bits  Bit Errors  BER(%)  Total Blocks  Block Errors  BLER(%)  time(Sec.)\")\n",
    "    print(\"---------  ----------  ----------  ------  ------------  ------------  -------  ----------\")\n",
    "    for ebNoDb in ebNoDbs:                              # For each Eb/No value in ebNoDbs\n",
    "        snrDb = ebNoDb + 10*np.log10(pdsch.modems[0].qm * codeRate * bwp.dataTimeRatio) # Eb/No -> SNR\n",
    "        random.setSeed(123)                             # Making the results reproducible for each Eb/No\n",
    "        t0 = time.time()                                # Start time for each Eb/No\n",
    "        carrier.slotNo = 0\n",
    "\n",
    "        # Creating a CdlChannel object:\n",
    "        channel = CdlChannel('C', delaySpread=300, carrierFreq=4e9, dopplerShift=5,\n",
    "                             txAntenna = AntennaPanel([2,4]),  # 8 TX antenna\n",
    "                             rxAntenna = AntennaPanel([1,2]),  # 2 RX antenna\n",
    "                             seed = 123,\n",
    "                             timing = \"nearest\")  \n",
    "\n",
    "        blockErrors = 0\n",
    "        totalBlocks = 0\n",
    "        bitErrors = 0\n",
    "        totalBits = 0\n",
    "\n",
    "        for slotNo in range(numSlots):\n",
    "            grid = pdsch.getGrid()                       # Create a resource grid already populated with DMRS \n",
    "            txBlockSize = pdsch.getTxBlockSize(codeRate) # Calculate the Transport Block Size\n",
    "            txBlock = random.bits(txBlockSize[0])        # Create random binary data\n",
    "            numBits = pdsch.getBitSizes(grid)            # Actual number of bits available in the resource grid\n",
    "\n",
    "            # Perform the segmentation, rate-matching, and encoding\n",
    "            rateMatchedCodeWords = ldpcEncoder.getRateMatchedCodeWords(txBlock, numBits[0])\n",
    "\n",
    "            pdsch.populateGrid(grid, rateMatchedCodeWords)   # Map/modulate the data to the resource grid\n",
    "\n",
    "            # Store the indexes of the PDSCH data in pdschIndexes to be used later.\n",
    "            pdschIndexes = pdsch.getReIndexes(grid, \"PDSCH\")   \n",
    "\n",
    "            # Getting the Precoding Matrix, and precoding the resource grid\n",
    "            channelMatrix = channel.getChannelMatrix(bwp)           # Get the channel matrix\n",
    "            precoder = pdsch.getPrecodingMatrix(channelMatrix)      # Get the precoder matrix from the PDSCH object\n",
    "            precodedGrid = grid.precode(precoder)                   # Perform the precoding\n",
    "\n",
    "            if freqDomain:\n",
    "                rxGrid = precodedGrid.applyChannel(channelMatrix)   # Apply the channel in frequency domain\n",
    "                rxGrid = rxGrid.addNoise(snrDb=snrDb)               # Add noise\n",
    "            else:\n",
    "                txWaveform = precodedGrid.ofdmModulate()            # OFDM Modulation\n",
    "                maxDelay = channel.getMaxDelay()                    # Get the max. channel delay\n",
    "                txWaveform = txWaveform.pad(maxDelay)               # Pad with zeros\n",
    "                rxWaveform = channel.applyToSignal(txWaveform)      # Apply channel in time domain\n",
    "                noisyRxWaveform = rxWaveform.addNoise(snrDb=snrDb, nFFT=bwp.nFFT)  # Add noise\n",
    "                offset = channel.getTimingOffset()                  # Get timing info for synchronization\n",
    "                syncedWaveform = noisyRxWaveform.sync(offset)       # Synchronization\n",
    "                rxGrid = syncedWaveform.ofdmDemodulate(bwp)         # OFDM demodulation\n",
    "\n",
    "                \n",
    "            if chanEstMethod == \"Perfect\":                          # Perfect Channel Estimation\n",
    "                estChannelMatrix = channelMatrix @ precoder[None,...]\n",
    "            else:                                                   # LS + Interpolation Channel Estimation\n",
    "                estChannelMatrix, noiseEst = rxGrid.estimateChannelLS(pdsch.dmrs, polarInt=False, \n",
    "                                                                      kernel='linear')\n",
    "                                                                      \n",
    "            eqGrid, llrScales = rxGrid.equalize(estChannelMatrix)            # Equalization\n",
    "            llrs = pdsch.getLLRsFromGrid(eqGrid, pdschIndexes, llrScales)    # Demodulation (to LLRs)\n",
    "            rxCodedBlocks = ldpcDecoder.recoverRate(llrs[0], txBlockSize[0]) # Recovering Rate\n",
    "            decodedBlocks = ldpcDecoder.decode(rxCodedBlocks, numIter=20)    # LDPC Decoding\n",
    "            decodedTxBlockWithCRC, crcMatch = ldpcDecoder.checkCrcAndMerge(decodedBlocks) # Merge blocks\n",
    "            decodedTxBlock = decodedTxBlockWithCRC[:-24]                     # remove transport block CRC\n",
    "            blockErrors += len(crcMatch)-sum(crcMatch)                       # Number of Block errors\n",
    "            bitErrors += np.abs(decodedTxBlock-txBlock).sum()                # Number of bit errors\n",
    "            totalBlocks += len(crcMatch)\n",
    "            totalBits += len(txBlock)\n",
    "            \n",
    "            ber = bitErrors*100/totalBits\n",
    "            bler = blockErrors*100/totalBlocks\n",
    "            print(\"\\r  %3d      %8d    %8d    %6.2f    %8d      %8d     %6.2f     %6.2f\"\n",
    "                  %(ebNoDb, totalBits, bitErrors, ber, totalBlocks, blockErrors, bler, time.time()-t0), end='')\n",
    "\n",
    "            carrier.goNext()\n",
    "            channel.goNext()\n",
    "\n",
    "        dt = time.time()-t0\n",
    "        results[chanEstMethod][snrDb] = {\"totalBits\":  totalBits, \n",
    "                                         \"bitErrors\":  bitErrors, \n",
    "                                         \"BER\":        bitErrors*100/totalBits,\n",
    "                                         \"totalBlocks\":totalBlocks, \n",
    "                                         \"blockErrors\":blockErrors, \n",
    "                                         \"BLER\":       blockErrors*100/totalBlocks,\n",
    "                                         \"Time\":       dt}\n",
    "        print(\"\\r  %3d      %8d    %8d    %6.2f    %8d      %8d     %6.2f     %6.2f\"\n",
    "              %(ebNoDb, totalBits, bitErrors, ber, totalBlocks, blockErrors, bler, time.time()-t0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8f3a9a54",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the results in a couple of plots:\n",
    "logGraph = False\n",
    "def ebNo2Snr(ebNo): return ebNo + 10*np.log10(pdsch.modems[0].qm * codeRate * bwp.dataTimeRatio)\n",
    "\n",
    "# Bit Error Rate:\n",
    "for i,chanEstMethod in enumerate(['Perfect', 'LS']):\n",
    "    bers = [results[chanEstMethod][ebNo2Snr(ebNoDb)][\"BER\"] for ebNoDb in ebNoDbs]\n",
    "    plt.plot(ebNoDbs, bers, label=chanEstMethod)\n",
    "plt.legend()\n",
    "plt.title(\"Bit Error Rate for different mothods of Channel Estimation.\");\n",
    "plt.grid()\n",
    "plt.xlabel(\"eb/No\")\n",
    "plt.xticks(ebNoDbs)\n",
    "plt.ylabel(\"BER (%)\")\n",
    "if logGraph: plt.yscale('log')\n",
    "plt.show()\n",
    "\n",
    "# Block Error rate\n",
    "for i,chanEstMethod in enumerate(['Perfect', 'LS']):\n",
    "    blers = [results[chanEstMethod][ebNo2Snr(ebNoDb)][\"BLER\"] for ebNoDb in ebNoDbs]\n",
    "    plt.plot(ebNoDbs, blers, label=chanEstMethod)\n",
    "plt.legend()\n",
    "plt.title(\"Block Error Rate for different mothods of Channel Estimation.\");\n",
    "plt.grid()\n",
    "plt.xlabel(\"eb/No\")\n",
    "plt.xticks(ebNoDbs)\n",
    "plt.ylabel(\"BLER (%)\")\n",
    "if logGraph: plt.yscale('log')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acd64b1c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}