{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "59ab6898-615f-4bb7-a223-ad2c63fe3515",
   "metadata": {},
   "source": [
    "# Advanced PDSCH Configuration and End-to-End Processing\n",
    "\n",
    "This notebook demonstrates several advanced Physical Downlink Shared Channel (PDSCH) configuration options using **NeoRadium** and shows how these features affect resource mapping, transmission, channel estimation, and decoding.\n",
    "\n",
    "The example configures a PDSCH transmission with:\n",
    "\n",
    "- A **non-contiguous PRB allocation**\n",
    "- **Interleaved resource allocation**\n",
    "- **Reserved PRBs** that are excluded from PDSCH transmission\n",
    "- Multiple **CSI-RS resources**\n",
    "- **DMRS** and **PTRS** reference signals\n",
    "- A **two-layer MIMO transmission**\n",
    "- **LDPC channel coding**\n",
    "\n",
    "The notebook first constructs and visualizes the PDSCH resource grid, highlighting how data, reference signals, CSI-RS resources, and reserved resource elements are mapped. Random transport blocks are then LDPC encoded, modulated, and mapped onto the configured PDSCH resources.\n",
    "\n",
    "The transmitted signal is precoded and passed through a CDL fading channel with additive white Gaussian noise (AWGN). At the receiver, the notebook demonstrates two recovery approaches:\n",
    "\n",
    "1. **Perfect channel knowledge**, using the exact effective channel matrix.\n",
    "2. **Practical channel estimation**, using DMRS-based channel estimation and interpolation.\n",
    "\n",
    "The recovered transport blocks are decoded and verified using CRC checks, while channel-estimation quality is evaluated through normalized mean-square error (NMSE) metrics. The notebook therefore provides a complete example of how advanced PDSCH configuration options interact within an end-to-end 5G NR downlink transmission chain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0b2b804d-f26f-4029-8821-9cde54aba91b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from neoradium import BandwidthPart, PDSCH, AntennaPanel, CdlChannel, CsiRsConfig, ReservedPrbSet, random\n",
    "from neoradium.utils import getNmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "10485c79-992d-4dae-bfd7-ba69cdd6c637",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "PDSCH Properties:\n",
      "  mappingType:               A\n",
      "  nID:                       1\n",
      "  rnti:                      1\n",
      "  numLayers:                 2\n",
      "  numCodewords:              1\n",
      "  modulation:                16QAM\n",
      "  PRG Size:                  2\n",
      "  portSet:                   0   1  \n",
      "  symSet:                    1   2   3   4   5   6   7   8   9   10 \n",
      "  prbSet:                    0   1   2   3   4   5   6   7   16  17  18  19  20  21  22  23 \n",
      "  DMRS:\n",
      "    configType:              1\n",
      "    nIDs:                    []\n",
      "    scID:                    0\n",
      "    sameSeq:                 True\n",
      "    symbols:                 Single\n",
      "    typeA1stPos:             2\n",
      "    additionalPos:           1\n",
      "    cdmGroups (port:cdm):    0:0  1:0 \n",
      "    deltaShifts (port:cdm):  0:0  1:0 \n",
      "    numCdmGroupsWithoutData: 2\n",
      "    symSet:                  2   9\n",
      "    REs (before shift):      0 2 4 6 8 10\n",
      "    epreRatioDb:             -3 (dB)\n",
      "  PTRS:\n",
      "    timeDensity:             1\n",
      "    freqDensity:             2\n",
      "    reOffset:                0\n",
      "    portSet:                 [0]\n",
      "    epreRatio:               0\n",
      "    symSet:                  1   3   4   5   6   7   8  10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "random.setSeed(123)                                         # Make the results reproducible\n",
    "\n",
    "# Create a bandwidth part\n",
    "bwp = BandwidthPart(numRbs = 24,                            # 24 resource blocks\n",
    "                    spacing = 30,                           # 30 kHz subcarrier spacing\n",
    "                    interleavingBundleSize = 2)             # Enable interleaving with bundle size 2\n",
    "\n",
    "# Create a reserved PRB set (all resource elements on OFDM symbol 8 in PRBs 0 and 6)\n",
    "reservedPrbSet = ReservedPrbSet(bwp, prbs=[0,6], symbols=[8], pattern=\"0101\")\n",
    "# reservedPrbSet.print()                                    # Uncomment this to see details of reserved PRBs\n",
    "\n",
    "# Create a CDL-C channel model\n",
    "channel = CdlChannel(bwp, profile='C', delaySpread=100, carrierFreq=4e9, dopplerShift=10,\n",
    "                     txAntenna=AntennaPanel([1,4], polarization='x', beamWidth=[65,65]),  # 8 TX antennas\n",
    "                     rxAntenna=AntennaPanel([1,2], polarization='x', beamWidth=[65,360])) # 4 RX antennas (Omnidirectional)\n",
    "# channel.print()                                           # Uncomment this to see details of the channel model\n",
    "\n",
    "# Create a typical CSI-RS configuration for beam sweeping, beam probing, and CSI-feedback:\n",
    "csiRsConfig = CsiRsConfig.beamformingConfig(bwp, channel.txAntenna.numPorts)\n",
    "# csiRsConfig.print()                                       # Uncomment this to see details of CSI-RS configuration\n",
    "\n",
    "# Create a 2-layer PDSCH\n",
    "pdsch = PDSCH(bwp, numLayers=2,                             # Two transmission layers\n",
    "              csiRsConfig=csiRsConfig,                      # The CSI-RS configuration\n",
    "              reservedPrbSets=[reservedPrbSet],             # A list of 'ReservedPrbSet' objects\n",
    "              prbSet=list(range(8))+list(range(16,24)),     # A non-contiguous set of PRBs: 0..7 , 16..23\n",
    "              symStart=1, symLen=10,                        # Use OFDM symbols 1 to 10\n",
    "              modulation=\"16QAM\",                           # Modulation Scheme\n",
    "              prgSize=2)                                    # enable precoding RB groups (PRGs) with size 2\n",
    "# DM-RS configuration\n",
    "pdsch.setDMRS(configType=1,                                 # Config Type 1 \n",
    "              typeA1stPos=2,                                # First DMRS symbol is 2\n",
    "              additionalPos=1,                              # One more symbol for DMRS (a total of 2 OFDM symbols)\n",
    "              symbols=1,                                    # Single DMRS symbols\n",
    "              numCdmGroupsWithoutData=2)                    # Make sure unused REs in the DMRS OFDM symbols are \n",
    "                                                            # not assigned to PDSCH.\n",
    "# Create PT-RS\n",
    "pdsch.setPTRS()                                             # Use all default PTRS settings\n",
    "\n",
    "pdsch.print()                                               # Print PDSCH information\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a780b832-74b3-4e5b-8259-1712a124654b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "LDPC Encode/Decode Properties:\n",
      "  Num layers:         2\n",
      "  Num codewords:      1\n",
      "  numIter:            5\n",
      "  nRef:               0\n",
      "  Modulation:         16QAM\n",
      "  Coderate:           772/1024\n",
      "  TBS:                8712\n",
      "  numLayers:          2\n",
      "  Base Graph:         1\n",
      "  Code Block Size:    4576\n",
      "  Num Code Blocks:    2\n",
      "  Lifting Size:       208\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Create an LDPC codec\n",
    "ldpc = pdsch.getLdpcCodec(coderates=772/1024)               # Use the info already in PDSCH to create LDPC codec\n",
    "ldpc.print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9297a9c5-9476-43e0-8a0d-23c2767959ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of resource elements:\n",
      "  GridSize:       5376\n",
      "  UNASSIGNED:     1536\n",
      "  RESERVED:       48\n",
      "  NO_DATA:        448\n",
      "  PDSCH:          2640\n",
      "  DMRS:           384\n",
      "  PTRS:           64\n",
      "  CSIRS_NZP(1):   32\n",
      "  CSIRS_NZP(2):   32\n",
      "  CSIRS_NZP(3):   32\n",
      "  CSIRS_NZP(4):   32\n",
      "  CSIRS_NZP(5):   32\n",
      "  CSIRS_NZP(6):   32\n",
      "  CSIRS_NZP(7):   32\n",
      "  CSIRS_NZP(8):   32\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and initialize PDSCH's internal resource grid, populate it with DM-RS and PT-RS, and \n",
    "# reserve REs for CSI-RS and reserved PRBs\n",
    "pdsch.initGrid()\n",
    "\n",
    "# Print statistics about the number of resource elements for each type of data in the PDSCH:\n",
    "print(\"Number of resource elements:\")\n",
    "stats = pdsch.grid.getStats() \n",
    "for key, value in stats.items(): print(f\"  {key+\":\":<15s} {value}\")\n",
    "\n",
    "# Draw a map of PDSCH's resource grid\n",
    "pdsch.grid.drawMap(ports=[0,1],                             # Draw both layers (ports)\n",
    "                   rbRange=(0,pdsch.grid.numRbs));          # Draw all PRBs in the grid\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b882ea-f0ae-467c-a51c-6e614046c5a3",
   "metadata": {},
   "source": [
    "The figure above shows the PDSCH resource allocation before precoding and transmission. The grid contains two layers corresponding to the two configured PDSCH transmission layers.\n",
    "\n",
    "* PDSCH occupies OFDM symbols 1 through 10.\n",
    "* Resource blocks 0 and 8 are reserved in OFDM symbol 8. Recall that the reserved-PRB configuration was defined using PRBs 0 and 6. Because PRB interleaving is enabled, the resource blocks are rearranged, and PRB 6 maps to virtual resource block (VRB) 8. You can verify this by printing `pdsch.prb2Grb[6]`, which should return 8. The `prb2Grb` mapping translates physical resource blocks (PRBs) to GRBs, which are the resource-block indices used in the PDSCH's internal resource grid before interleaving. Later, after precoding and transmission-grid construction, you will see these resources mapped back to their configured PRB locations.\n",
    "* DM-RS is present in OFDM symbols 2 and 9.\n",
    "* The resource elements not occupied by DM-RS within the DM-RS symbols are marked as `NO_DATA` and cannot be used for PDSCH transmission. Try changing `numCdmGroupsWithoutData` to 1 and observe how the allocation of these resource elements changes.\n",
    "* CSI-RS occupies OFDM symbol 4. In this symbol, eight resource elements within each resource block are allocated to CSI-RS.\n",
    "* PT-RS is configured only on layer 0 (port 0).\n",
    "* On layer 0, PT-RS is present in all PDSCH symbols except those that contain DM-RS.\n",
    "* On layer 0, PT-RS is allocated to resource blocks 1, 3, 5, and so on. Within each of these resource blocks, PT-RS occupies the first resource element.\n",
    "* On layer 1, all resource elements corresponding to PT-RS allocations on layer 0 are marked as `NO_DATA`. Consequently, these resource elements are not available for PDSCH transmission on layer 1.\n",
    "* The PDSCH internal resource grid remains contiguous even though the transmission uses a non-contiguous PRB allocation. This becomes more apparent later when the resources are mapped from the internal grid to their final PRB locations in the transmitted resource grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a8499ec3-f2ac-4228-b0a0-ccc095fc176e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of channel matrix: 14 × 288 × 4 × 8\n",
      "Precoder has 8 PRB groups. Shape of precoder for each PRB group: 8 × 2\n"
     ]
    }
   ],
   "source": [
    "numBits = pdsch.getBitCapacity()            # Number of PDSCH data bits in the resource grid for each codeword\n",
    "\n",
    "# Create random transport block bitstreams for each codeword\n",
    "txBlocks = [random.bits(tbs) for tbs in ldpc.txBlockSizes]  # Get TBS from the LDPC codec object for each codeword\n",
    "\n",
    "# Perform LDPC rate-matching and encoding\n",
    "rateMatchedCodeBlocks = ldpc.encode(txBlocks, numBits)  \n",
    "\n",
    "# Map the encoded bitstreams to the PDSCH resource elements \n",
    "pdsch.setPdschData(rateMatchedCodeBlocks)\n",
    "\n",
    "# Obtain a channel matrix from the channel model and derive a precoding matrix from it.\n",
    "# Note that here we are assuming perfect channel knowledge for the precoding calculation\n",
    "channelMatrix = channel.getChannelMatrix()\n",
    "print(f\"Shape of channel matrix: {' × '.join(str(x) for x in channelMatrix.shape)}\")\n",
    "# Since prgSize was set to two, the following precoder is a list of precoder matrices for \n",
    "# subbands of size 2 RBs. Each item in the list is a tuple of (groupPRBs, groupPrecoder)\n",
    "precoder = pdsch.getPrecodingMatrix(channelMatrix)\n",
    "print(f\"Precoder has {len(precoder)} PRB groups. Shape of precoder for each PRB group: {precoder[0][1].shape[0]} × {precoder[0][1].shape[1]}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bad92f8b-e178-4f46-aca7-9fdac6fb7fd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of transmitted resource grid: 8 × 14 × 288\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a transmitted resource grid:\n",
    "txGrid = bwp.createGrid(channel.txAntenna.numPorts)\n",
    "print(f\"Shape of transmitted resource grid: {' × '.join(str(x) for x in txGrid.shape)}\")\n",
    "\n",
    "# Map and precode the PDSCH onto the transmitted resource grid. Interleaving and VRB to PRB mapping happen here.\n",
    "pdsch.precodeTo(txGrid, precoder)\n",
    "\n",
    "# Now we need to set the CSI-RS values in the transmitted resource grid. Note that CSI-RS is independent of the PDSCH allocation.\n",
    "# We first get the CSI-RS resources for current slot of communication. Since the first set of CSI-RS resources are used for\n",
    "# beam sweeping, we get a set of beams and their corresponding precoders using the AntennaPanel's 'getSweepingBeams' function.\n",
    "# We then precode the CSI-RS values using each beam's precoder and place the precoded CSI-RS symbols into 'txGrid'.\n",
    "csiResources = csiRsConfig.getResources()\n",
    "csiRsPrecoders, beams = channel.txAntenna.getSweepingBeams(1, 8, polStrategy=\"equal\")\n",
    "for csiSetId, setResources in csiResources.items():\n",
    "    for b, (resourceId, (lIdx, kIdx, csiRsValues)) in enumerate(setResources.items()):\n",
    "        # Sweeping CSI-RS use only one port. Precoder shape is: numTxAntenna x 1\n",
    "        # CSI-RS values for each CSI-RS resource is a 1 x numREs vector where numREs are the number of CSI-RS resource elements\n",
    "        # The precoded matrix's shape is: numTxAntenna x numREs which is copied to the transmitted resource grid.\n",
    "        txGrid[:,lIdx, kIdx] = (csiRsPrecoders[:,b:b+1] @ csiRsValues, \"CSIRS_NZP\", resourceId)   \n",
    "\n",
    "# Draw the PRB map of the transmitted resource grid (first antenna port only)\n",
    "txGrid.drawMap(rbRange=(0,txGrid.numRbs));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c48908bf-1f34-4720-9f00-10763b139358",
   "metadata": {},
   "source": [
    "The figure above shows the transmitted resource grid after precoding. Unlike the previous allocation map, this grid contains the actual complex-valued symbols that will be transmitted from each antenna port.\n",
    "\n",
    "* The figure shows the transmitted resource grid in physical resource block (PRB) order.\n",
    "* As configured, the PDSCH occupies PRBs 0 through 7 and PRBs 16 through 23.\n",
    "* The reserved resources appear in their configured PRB locations, namely PRBs 0 and 6.\n",
    "* PDSCH, DM-RS, and PT-RS resources are distributed across the bandwidth according to the configured interleaving pattern.\n",
    "* CSI-RS occupies resource elements across all PRBs, including those that are not allocated to the PDSCH.\n",
    "\n",
    "This is the signal representation that is ultimately converted to the time domain, transmitted through the wireless channel, and processed by the receiver."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "279d1bed-f3b7-4a59-914d-936684f3c559",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the noisy received resource grid: 4 × 14 × 288\n"
     ]
    },
    {
     "data": {
      "image/png": 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KiSfV50yfPt2WL1/u/q0ZmZT40UMJOD30uh6azUvrwH+Pv7w/TXWPHj3c+vj444/dLFGpKEGqmYz896Syzz77xGb1qsgkgE8JMMWfzJQpU2IxlGbmN5+mM5fOnTvbrFmzkpajfr8SX/HlmK7S1Ok1a9a47y8Nv+7pc1PNIKaYd9hhh0LLl6es4mlfeOCBB9qyZctcQlCJylQz0x1xxBHu77Bhw6xnz55upi1NLV8e/uyLJSV7/JnFlFQu60xrmVoPZaVZIg855BC3D1C9VXnq9yVz9tlnuyTaddddZ+3bty/3dwNAlDFdPQDA3RHWQxc5N9xwg40dO9b9Wyfdl19+uZ188snurmxJNL22aKrm4qgFQ0kJlIp28MEHu9+oixe1yPETXrq4SKdFx/333x/7v1oQ6ILev2jUdOpqRaSL4FSKKxtNw66LWb3fL8uKUlHrRtOyV4RUn6Opzn2lbcmlC3jf888/71ojzZ49226//Xb30IW2ElFq2dKvX79C02zHl3NxZeOXS6bqbXHlqunFZYsttkjrM/2yVIuo3NzctMoxHcWVW/PmzWP/Lm2dLk9dLWtZxXv66adjyfO77rqr2KSL9o1KwD3wwAP29ttvu4f/uw844ADX8kWJ5HT4iWolIdXiK1ViRK+LWrmlWqY8Knt/rhZASuj9/PPPtu2229qHH37o9onJPPnkk/bBBx+4hHx5WoYBAP6HxBAAoIh9993Xrr76atdMXxcHn332WakubmrVqlXoTnYqfgsNf/nKoIu7M88802688UY7/fTT3cWHLir8FlIl+fTTT2NJofPOO89djCR2hVLXu5KSGf5vT0YXgX7ZVXTZBHndxIu/wFUyozQtIeJbfHXq1Mm1glG3Fl2k//e//3UtZCZPnuwed999t7355pux7nzxv7O4solfb5VdNn7Ll+LqTnFlqeSlulaVRImQsigurrKUW3nqalnLKrF71JAhQ1zrFSXI1YIoVSs7UctL7RNGjBjhWqgpUaQWl2rtoodaK6bT1UktEUUJ4t9++61Qci2eWnpKmzZtLBMqc5+hslZSSK2t1ApTNyeKq7OXXnppLFmv1nCJ/FaCanmkrmmi/XWyrqIAABJDAIAU1OXIp4uT0vDvHP/www/FLue/3qJFi0otf929HzhwYOxCIp3WQv74Gbvssos9+OCDSZdRsqkkxXVD0h1zdW/LRNkEfd344rtTaYyTZGPXlKRmzZqudZAefksYjZ+jRKcups8444xYt6b4lkD67f6YW4niyy2+bPxuP8WNn6OWZOUtE3WRKm1XrPj3ffnll64cHnvsMcuU4up0/GulrVPlqatlLat4Shoee+yxdtBBB7nWVocddphLJhY35s/WW2/tkul6KMGrccbuu+8+Gz58uCv7ww8/3H1OaShh7dPnaJydZJToTFw+jPsM1RElhdTasjRJIcnPz3d/zzrrrGKX02f55aNEsVqOAgCKYowhAIiY4ro5xYtPBsV3vSmO3wrjq6++SjnOhu6kq/VN/PLxF/TiJ0cqmi761VpI3RS23377lIOaFtctp7iBl994440SP0eD8Kb6fbr4FHVN88fKqSjlXTeVRYkgPzn06quvVshnNmnSxE455RTXCkSUGPTXZ8eOHWNj7BQ3GLofi+pQ/AWwPju+9UYyEydOLFf8/thH48aNc4Mhp9PyT5QUK+3Az2Xh19viXtOFvj84e0n8uqff6ycAEqkVyPfff19o+fKUVbLEuLoqqSuTkgtKzviDQpempdbOO+/sWgtpXyPjx48v9Xer26PffSvVNuCPFSUa2yhdpdnXlmc9lJa6fKo1anxSqKSua6JyLe7hJ/HUmtB/Lp2x5AAgakgMAUDEaPwMzTBT3KxNamExaNAg92+NLVTa7laavUcDAuti4/zzzy8yeKr/vLom6HM18HM8/wK9PBd0JdHYFLqY0VhDqcavSMbvzvHJJ5/ExvaIp64imsmoNF0m7rzzziLPL1y40G699dZYORbXdaUsyrtuKtOFF17o/qoO+oMJpxK/LjQ7U3EXuv7FrS7c/fLVv/0Zl7QOv/jiiyLv03pVyw9Ra6N4O+64Y+wCV2NXJVJXtnRn90uk+HRRqy47ffr0cb8zmcTntazGwVLdUpeb4spGr6X63JKo69QzzzxT5Hkl4DT2jpx66qml/jwlbNUSS92Bks2mpuT2BRdcEEtax7fEKWtZJdOlSxc3zo2+Q8kRDYqcuO0n2xf4FIOfTPIHSi8NdZ/065nqnVp9xVOLJH9WvqZNm7oxtdJVmn1tedZDaah+KJGnGJS4+eijj0qVFBJ/JshUDz+xruSo/9wee+yRVnwAEClpTm8PAAi5Rx55xNPuv0aNGt5hhx3mPfTQQ964ceO8qVOneqNHj/auu+46b7PNNnPL6HHjjTcW+Yw99tjDvTZw4MAir913332x9+61117eK6+84n3xxRfea6+95u23336x126//fYi7x0wYIB7bZNNNvGGDRvmTZkyxfvmm2/cY926daX+jccee6z7nIMOOqhMZZPs8Kjf4L+2yy67eC+99JKL7+233/b69OnjVatWzZWpH3+irbbaKlbuWvass87yPvzwQ/cZQ4YM8Vq2bOleb9iwoTd37ty04o6PXd+TSnnWjR+/vqc8SvM5a9ascfFpudq1a3t9+/b1Ro0a5erof//7X2/kyJHe4MGDvR49engdO3aMvU/LqO6eccYZ3pNPPum988477j1jx471brrpJq9Ro0buMw855JBC37dkyRJv8803d681btzYu+WWW7xPP/3U++STT7zrr7/eq1+/fqxsCwoKCr139erVXosWLdzrTZs2ddvTpEmTvI8//tjV53r16sXqRbNmzcq03kTbg79+OnTo4D344IMuRj1eeOEFr3///u63J3r55ZddfdP7dtppJ/e+iRMnuvWuuqtyUl3Ue1V+6WjQoEGsTtesWdO7+OKLvfHjx3uff/65d88997jy8H/30qVLU9aFxx9/POW+QI9DDz3Ue/PNN13MI0aM8HbdddfYayqXiior//cklsO0adNiv0X7vvg6oPqnunrbbbe5eqnv0Pp//vnnvT333DNWPl9//XVaZbts2TKvVatW7v0bbbSRd//997tyHTNmjHfwwQfHfp/WX1loG9H7W7du7b344ouubLWf/fbbbytsPRTn+++/95o3b+7eq3WhbdTf1yd7/P3332l9vn+MOvLII9N6HwBEFYkhAIiY4cOHe9WrV4+d0Kd61KpVy7vmmmu8DRs2pJUYEiWT/IvhxIe+++qrr076vp9++slr0qRJ0vfp4j2biSG54YYbUpaXLnKVUCkpMaQybd++fdLPUFLigw8+SCvmdBMMZV03lZkYkuXLl3snnXRSifV0xx13jL3n3XffLbFu77zzzt6vv/5a5Pu+++47b8stt0z5vm222cabM2dO0ljff/99lwBK9r5OnTrF6kV5EkPy1FNPxZIXyR5KICSjRIfqZHHlonJT+aXDj2XQoEGxxFriY9NNN3XJuWSKSwytX7/eu/DCC2NJrWT7JyWfKrKsUiWGRMkJP2G+2267efn5+e551b/iyrVu3bre0KFDvbJQnWzXrl3K9aVkZ1l99dVXLrbEz9W+oSLXQypa5yVt2/GPL7/8Mq3PJzEEAOlhVjIAiBiNtaLxMjTmhcY+0XgzGk9I3cfU3UFjqKjr2PHHH19oIOB4GitEy6caIPS6665z71e3NQ2Q+ueff7qxWDTuhrp3dO7cOen7NLuOuk088sgj7n2aEc2fEccfE6O0s/qoa0Jx4wGl6l7hjwmSzPXXX2977rmnm+pe3SDU5UNlpGmpNXaRuhzp/cV1UdM02urG9vjjj7sxTH799Vc3Hoa6OagrV1mn2fZj92c0SqWs60Zj/6iLmd8FpaxK+zmaPUhTz6vLjGZ7UpcyTYetstKguO3bt3dde7p16xZ7z4EHHujGSdI4TloXGrdEYwmpXut7Ve+PPvropDOdaXyT6dOnu3Fh3nnnHTdmkLqZqQ7pfZqWPFUd3H///d061aDkinPVqlVuPSo+zYSnLjJaN+r2U9b1JupepFi07rT96reqPPRedck58cQTk75Pg08rRpWnxnDRb1M3IHXb0XhJqnv6XM1elg6VmboZaTwelZ264qnLlcq8cePGLibV6VRjlBVXF7SONHizuoZpnWi/oBmmtKz2TyrXrbbaqkLLyv89ij2RtgutR20jikNdq/R7tb7VvVQDG6s7obpFqWxVRzXmjpZPtR8tieL55ptv3G949913XZdA1eUddtjB/X6/G2NZ6DPUbVK/QWWr/bm6lybW8fKuh7LuaxOl27XWP0ZlasY2AKhqqik7lO0gAACo6pTI0Ow7Snr1798/2+EAAAAADoNPAwAAAAAARBSJIQAAAAAAgIgiMQQAAAAAABBRDD4NAEAlqKjBmwEAAICKxODTAAAAAAAAEUVXMgAAAAAAgIgiMQQAAAAAABBRJIYAAAAAAAAiisQQAAAAAABARJEYAgAAAAAAiCgSQwAAAAAAABFFYggAAAAAACCiamY7gKDYsGGDLVy40Bo1amTVqlXLdjgAAAAAACAgPM+z5cuXW4sWLax69arVxobE0P9TUqhVq1bZXRsAAAAAACCwFixYYC1btrSqhMTQ/1NLIbnvvvtsxx13tCCbNWuW9e3b1x577DHr2LGjBRmxUrbUA+pA2OpA2OIlVso1THUgbPESK+UapjoQtniJlXINUx2Qr776yi666KJY7qAqITH0//zuY0oK7bXXXhZkDRs2dH+7dOlieXl5FmTEStlSD6gDYasDYYuXWCnXMNWBsMVLrJRrmOpA2OIlVso1THUgXlUceqZqdYwDAAAAAABAqZEYAgAAAAAAiCgSQwAAAAAAABFFYggAAAAAACCiSAwBAAAAAABEFIkhAAAAAACAiCIxBAAAAAAAEFEkhgAAAAAAACKqpgXUlClT7OWXX7Z58+bZMcccYyeccELS5QoKCmzkyJH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",
      "text/plain": [
       "<Figure size 1200x600 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": [
    "# Apply the CDL channel model to the transmitted resource grid in frequency domain\n",
    "rxGrid = txGrid.applyChannel(channelMatrix)\n",
    "\n",
    "# Add AWGN noise to get a noisy received resource grid\n",
    "noisyRxGrid = rxGrid.addNoise(snrDb=5)   \n",
    "print(f\"Shape of the noisy received resource grid: {' × '.join(str(x) for x in noisyRxGrid.shape)}\")\n",
    "\n",
    "# Draw the PRB map of the noisy received resource grid (first antenna port only)\n",
    "rxGrid.drawMap(rbRange=(0,noisyRxGrid.numRbs))\n",
    "plt.show()\n",
    "\n",
    "# Visualize the noisy received signal on the first receive antenna in the frequency domain\n",
    "plt.plot(np.abs(noisyRxGrid[0,1,:]))\n",
    "plt.title(\"The noisy received signal in frequency domain\\n(First PDSCH symbol and first receiver antenna)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99ca9e2b-f81a-4383-8789-2fdfee4a7e14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the effective channel matrix: 14 × 288 × 4 × 2\n",
      "Transport block CRC match:         True\n",
      "CRC matches for the code block(s): [ True  True]\n"
     ]
    }
   ],
   "source": [
    "# Assuming perfect channel knowledge, we can get the effective channel matrix using the 'getEffChannel' \n",
    "# function of the channel model. This effective channel includes the effects of precoding.\n",
    "# In practice, this channel is estimated using reference signals such as DM-RS.\n",
    "effChannelMatrix = channel.getEffChannel(channelMatrix, precoder)\n",
    "print(f\"Shape of the effective channel matrix: {' × '.join(str(x) for x in effChannelMatrix.shape)}\")\n",
    "\n",
    "# Equalize the noisy received resource grid\n",
    "eqGrid, llrScales = pdsch.equalize(noisyRxGrid, effChannelMatrix)\n",
    "\n",
    "# Demodulate to get log-likelihood ratios (LLRs)\n",
    "llrs = pdsch.getLLRs(eqGrid, llrScales)\n",
    "\n",
    "# Perform LDPC rate-recovery and decoding:\n",
    "decodedTxBlocks, crcMatch = ldpc.decode(llrs)\n",
    "print(f\"Transport block CRC match:         {crcMatch[0][0]}\")\n",
    "print(f\"CRC matches for the code block(s): {crcMatch[0][1:]}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b4511ed-2882-4141-aa68-a042db626bef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NMSE between the estimated and perfect channel (All PRBs):  0.719\n",
      "NMSE between the estimated and perfect channel (PDSCH RBs): 0.004\n",
      "NMSE between the equalized and the original resource grid:  0.196\n",
      "\n",
      "Transport block CRC match:         True\n",
      "CRC matches for the code block(s): [ True  True]\n"
     ]
    }
   ],
   "source": [
    "# We can also estimate the channel using DM-RS signals. The PDSCH's 'estimateChannel' method is\n",
    "# used here to estimate channel, and the the estimated channel matrix is used to equalize and decode\n",
    "# the received resource grid.\n",
    "estChannelMatrix, errVar = pdsch.estimateChannel(noisyRxGrid)\n",
    "print(f\"NMSE between the estimated and perfect channel (All PRBs):  \"\n",
    "      f\"{getNmse(effChannelMatrix, estChannelMatrix):.3f}\")\n",
    "\n",
    "# Since only the PRBs used by PDSCH are important, we can compare the channel matrices\n",
    "# only in those PRBs.\n",
    "preSet = (pdsch.prbSet[:,None]*12+np.arange(12)).flatten()      # subcarriers used by PDSCH\n",
    "print(f\"NMSE between the estimated and perfect channel (PDSCH RBs): \"\n",
    "      f\"{getNmse(effChannelMatrix[:,preSet], estChannelMatrix[:,preSet]):.3f}\")\n",
    "\n",
    "# Equalize the noisy received resource grid using the estimated channel matrix\n",
    "# Note that in this case we must use the 'errVar' returned by 'estimateChannel' for equalization\n",
    "eqGridEst, llrScales = pdsch.equalize(noisyRxGrid, estChannelMatrix, errVar)\n",
    "print(f\"NMSE between the equalized and the original resource grid:  \"\n",
    "      f\"{getNmse(eqGridEst.grid, pdsch.grid.grid):.3f}\\n\")\n",
    "\n",
    "# Demodulate to get log-likelihood ratios (LLRs)\n",
    "llrs = pdsch.getLLRs(eqGridEst, llrScales)\n",
    "\n",
    "# Perform LDPC rate-recovery and decoding\n",
    "decodedTxBlocks, crcMatch = ldpc.decode(llrs)\n",
    "print(f\"Transport block CRC match:         {crcMatch[0][0]}\")\n",
    "print(f\"CRC matches for the code block(s): {crcMatch[0][1:]}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "894027fd-41a6-476b-bf20-8537caa7be57",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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