examples/Realistic_Multiuser_MIMO_Simulations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "06da1e50-ad7f-41cd-b53b-6335b62f6c3a",
   "metadata": {},
   "source": [
    "# Realistic Multiuser MIMO OFDM Simulations\n",
    "In this notebook, you will learn how to setup realistic simulations\n",
    "of multiuser MIMO uplink transmissions. Multiple user terminals (UTs)\n",
    "are randomly distributed in a cell sector and communicate with a multi-antenna\n",
    "base station.\n",
    "\n",
    "\n",
    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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91114f45-9448-4a5d-b47c-8a6e8e4936dc",
   "metadata": {},
   "source": [
    "The block-diagramm of the system model looks as follows:\n",
    "\n",
    "\n",
    "![System Model](data:image/png;base64,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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6f1d65-8a8c-45ca-9e35-eb0e54983200",
   "metadata": {},
   "source": [
    "It includes the following components:\n",
    "\n",
    "- 5G LDPC FEC\n",
    "- QAM modulation\n",
    "- OFDM resource grid with configurable pilot pattern\n",
    "- Multiple single-antenna transmitters and a multi-antenna receiver\n",
    "- 3GPP 38.901 UMi, UMa, and RMa channel models and antenna patterns\n",
    "- LS Channel estimation with nearest-neighbor interpolation as well as perfect CSI\n",
    "- LMMSE MIMO equalization\n",
    "\n",
    "You will learn how to setup the topologies required to simulate such scenarios and investigate\n",
    "\n",
    "- the performance over different models, and\n",
    "- the impact of imperfect CSI.\n",
    "\n",
    "We will first walk through the configuration of all components of the system model, before simulating\n",
    "some simple uplink transmissions in the frequency domain. We will then simulate CDFs of the channel condition number\n",
    "and look into frequency-selectivity of the different channel models to understand the reasons for the observed performance differences.\n",
    "\n",
    "It is recommended that you familiarize yourself with the [API documentation](https://nvlabs.github.io/sionna/api/channel.html) of the `Channel` module\n",
    "and, in particular, the 3GPP 38,901 models that require a substantial amount of configuration.  The last set of simulations in this notebook take some time, especially when you have no GPU available. For this reason, we provide the simulation\n",
    "results directly in the cells generating the figures. Simply uncomment the corresponding lines to show this results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3001429c-d46a-47e4-8e5f-fa3afc402d73",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "* [GPU Configuration and Imports](#GPU-Configuration-and-Imports)\n",
    "* [System Setup](#System-Setup)\n",
    "* [Uplink Transmissions in the Frequency Domain](#Uplink-Transmissions-in-the-Frequency-Domain)\n",
    "    * [Compare Estimated and Actual Frequency Responses](#Compare-Estimated-and-Actual-Frequency-Responses)\n",
    "    * [Understand the Difference Between the Channel Models](#Understand-the-Difference-Between-the-Channel-Models)\n",
    "    * [Setup a Keras Model for BER simulations](#Setup-a-Keras-Model-for-BER-simulations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa4f4cfb-4a31-4a69-aaed-93d21fbb5d21",
   "metadata": {
    "tags": []
   },
   "source": [
    "## GPU Configuration and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ffb6a229",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of GPUs available : 2\n",
      "Only GPU number 0 used.\n"
     ]
    }
   ],
   "source": [
    "# Configure the notebook to use only a single GPU and allocate only as much memory as needed\n",
    "# For more details, see https://www.tensorflow.org/guide/gpu\n",
    "import tensorflow as tf\n",
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "print('Number of GPUs available :', len(gpus))\n",
    "if gpus:\n",
    "    gpu_num = 0 # Number of the GPU to be used\n",
    "    try:\n",
    "        tf.config.set_visible_devices(gpus[gpu_num], 'GPU')\n",
    "        print('Only GPU number', gpu_num, 'used.')\n",
    "        tf.config.experimental.set_memory_growth(gpus[gpu_num], True)\n",
    "    except RuntimeError as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9d3ff139",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time\n",
    "import pickle\n",
    "\n",
    "# Import Sionna\n",
    "try:\n",
    "    import sionna\n",
    "except ImportError as e:\n",
    "    # Install Sionna if package is not already installed\n",
    "    import os\n",
    "    os.system(\"pip install sionna\")\n",
    "    import sionna\n",
    "\n",
    "from sionna.mimo import StreamManagement\n",
    "\n",
    "from sionna.ofdm import ResourceGrid, ResourceGridMapper, LSChannelEstimator, LMMSEEqualizer\n",
    "from sionna.ofdm import OFDMModulator, OFDMDemodulator, ZFPrecoder, RemoveNulledSubcarriers\n",
    "\n",
    "from sionna.channel.tr38901 import Antenna, AntennaArray, CDL, UMi, UMa, RMa\n",
    "from sionna.channel import gen_single_sector_topology as gen_topology\n",
    "from sionna.channel import subcarrier_frequencies, cir_to_ofdm_channel, cir_to_time_channel\n",
    "from sionna.channel import ApplyOFDMChannel, ApplyTimeChannel, OFDMChannel\n",
    "\n",
    "from sionna.fec.ldpc.encoding import LDPC5GEncoder\n",
    "from sionna.fec.ldpc.decoding import LDPC5GDecoder\n",
    "\n",
    "from sionna.mapping import Mapper, Demapper\n",
    "\n",
    "from sionna.utils import BinarySource, ebnodb2no, sim_ber, QAMSource\n",
    "from sionna.utils.metrics import compute_ber"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15e1b3bb-5c41-4269-9bc8-1b4d7e48124c",
   "metadata": {
    "tags": []
   },
   "source": [
    "## System Setup\n",
    "\n",
    "We will now configure all components of the system model step-by-step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5182bedb-6726-4089-9e0e-9077056f3086",
   "metadata": {},
   "outputs": [],
   "source": [
    "scenario = \"umi\"\n",
    "carrier_frequency = 3.5e9\n",
    "direction = \"uplink\"\n",
    "num_ut = 4 \n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a20a279c-374a-48f6-8d31-3972a46d1dfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tf.random.set_seed(1)\n",
    "# Define the UT antenna array\n",
    "ut_array = Antenna(polarization=\"single\",\n",
    "                   polarization_type=\"V\",\n",
    "                   antenna_pattern=\"omni\",\n",
    "                   carrier_frequency=carrier_frequency)\n",
    "\n",
    "# Define the BS antenna array\n",
    "bs_array = AntennaArray(num_rows=1,\n",
    "                        num_cols=4,\n",
    "                        polarization=\"dual\",\n",
    "                        polarization_type=\"VH\",\n",
    "                        antenna_pattern=\"38.901\",\n",
    "                        carrier_frequency=carrier_frequency)\n",
    "\n",
    "# Create channel model\n",
    "channel_model = UMi(carrier_frequency=carrier_frequency,\n",
    "                    o2i_model=\"low\",\n",
    "                    ut_array=ut_array,\n",
    "                    bs_array=bs_array,\n",
    "                    direction=direction,\n",
    "                    enable_pathloss=False,\n",
    "                    enable_shadow_fading=False)\n",
    "\n",
    "# Generate the topology\n",
    "topology = gen_topology(batch_size, num_ut, scenario)\n",
    "\n",
    "# Set the topology\n",
    "channel_model.set_topology(*topology)\n",
    "\n",
    "# Visualize the topology\n",
    "channel_model.show_topology()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "88fdc090-fe1f-43c6-86b3-1698ec1d0bad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The number of transmitted streams is equal to the number of UT antennas\n",
    "num_streams_per_tx = 1\n",
    "\n",
    "# Create an RX-TX association matrix\n",
    "# rx_tx_association[i,j]=1 means that receiver i gets at least one stream\n",
    "# from transmitter j. Depending on the transmission direction (uplink or downlink),\n",
    "# the role of UT and BS can change. However, as we have only a single\n",
    "# transmitter and receiver, this does not matter:\n",
    "rx_tx_association = np.zeros([1, num_ut])\n",
    "rx_tx_association[0, :] = 1\n",
    "\n",
    "# Instantiate a StreamManagement object\n",
    "# This determines which data streams are determined for which receiver.\n",
    "# In this simple setup, this is fairly simple. However, it can get complicated\n",
    "# for simulations with many transmitters and receivers.\n",
    "sm = StreamManagement(rx_tx_association, num_streams_per_tx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d90fcba6-b37c-4d46-850a-1662eae3832e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rg = ResourceGrid(num_ofdm_symbols=14,\n",
    "                  fft_size=128,\n",
    "                  subcarrier_spacing=30e3,\n",
    "                  num_tx=num_ut,\n",
    "                  num_streams_per_tx=num_streams_per_tx,\n",
    "                  cyclic_prefix_length=20,\n",
    "                  pilot_pattern=\"kronecker\",\n",
    "                  pilot_ofdm_symbol_indices=[2,11])\n",
    "rg.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cdc0fa83-8e2b-495f-a5b8-5e8cb4267964",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_bits_per_symbol = 2 # QPSK modulation\n",
    "coderate = 0.5 # The code rate\n",
    "n = int(rg.num_data_symbols*num_bits_per_symbol) # Number of coded bits\n",
    "k = int(n*coderate) # Number of information bits\n",
    "\n",
    "# The binary source will create batches of information bits\n",
    "binary_source = BinarySource()\n",
    "qam_source = QAMSource(num_bits_per_symbol)\n",
    "\n",
    "# The encoder maps information bits to coded bits\n",
    "encoder = LDPC5GEncoder(k, n)\n",
    "\n",
    "# The mapper maps blocks of information bits to constellation symbols\n",
    "mapper = Mapper(\"qam\", num_bits_per_symbol)\n",
    "\n",
    "# The resource grid mapper maps symbols onto an OFDM resource grid\n",
    "rg_mapper = ResourceGridMapper(rg)\n",
    "\n",
    "# This function removes nulled subcarriers from any tensor having the shape of a resource grid\n",
    "remove_nulled_scs = RemoveNulledSubcarriers(rg)\n",
    "\n",
    "# The LS channel estimator will provide channel estimates and error variances\n",
    "ls_est = LSChannelEstimator(rg, interpolation_type=\"nn\")\n",
    "\n",
    "# The LMMSE equalizer will provide soft symbols together with noise variance estimates\n",
    "lmmse_equ = LMMSEEqualizer(rg, sm)\n",
    "\n",
    "# The demapper produces LLR for all coded bits\n",
    "demapper = Demapper(\"app\", \"qam\", num_bits_per_symbol)\n",
    "\n",
    "# The decoder provides hard-decisions on the information bits\n",
    "decoder = LDPC5GDecoder(encoder, hard_out=True)\n",
    "\n",
    "# OFDM CHannel\n",
    "ofdm_channel = OFDMChannel(channel_model, rg, add_awgn=True, normalize_channel=False, return_channel=True)\n",
    "channel_freq = ApplyOFDMChannel(add_awgn=True)\n",
    "frequencies = subcarrier_frequencies(rg.fft_size, rg.subcarrier_spacing)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb5aeee-618a-4ea8-95fe-536575ddc762",
   "metadata": {},
   "source": [
    "## Uplink Transmissions in the Frequency Domain\n",
    "\n",
    "We now simulate a batch of uplink transmissions. We keep references to the estimated and actual channel frequency responses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9ac4a821-136b-4e1a-9e2d-094c36c8a564",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BER: 6.103515625e-05\n"
     ]
    }
   ],
   "source": [
    "ebno_db = 10\n",
    "no = ebnodb2no(ebno_db, num_bits_per_symbol, coderate, rg)\n",
    "b = binary_source([batch_size, num_ut, rg.num_streams_per_tx, encoder.k])\n",
    "c = encoder(b)\n",
    "x = mapper(c)\n",
    "x_rg = rg_mapper(x)\n",
    "\n",
    "a, tau = channel_model(num_time_samples=rg.num_ofdm_symbols, sampling_frequency=1/rg.ofdm_symbol_duration)\n",
    "h_freq = cir_to_ofdm_channel(frequencies, a, tau, normalize=True)\n",
    "\n",
    "y = channel_freq([x_rg, h_freq, no])\n",
    "h_hat, err_var = ls_est ([y, no])\n",
    "x_hat, no_eff = lmmse_equ([y, h_hat, err_var, no])\n",
    "llr = demapper([x_hat, no_eff])\n",
    "b_hat = decoder(llr)\n",
    "print(\"BER: {}\".format(compute_ber(b, b_hat).numpy()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c7cca1c-8228-4668-a330-f0b853693cc2",
   "metadata": {},
   "source": [
    "### Compare Estimated and Actual Frequency Responses\n",
    "We can now compare the estimated frequency responses and ground truth:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d8a5a83e-f1ee-4e74-827f-f4b339ecc05b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In the example above, we assumed perfect CSI, i.e.,\n",
    "# h_hat correpsond to the exact ideal channel frequency response.\n",
    "h_perf = remove_nulled_scs(h_freq)[0,0,0,0,0,0]\n",
    "\n",
    "# We now compute the LS channel estimate from the pilots.\n",
    "h_est = h_hat[0,0,0,0,0,0]\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(np.real(h_perf))\n",
    "plt.plot(np.imag(h_perf))\n",
    "plt.plot(np.real(h_est), \"--\")\n",
    "plt.plot(np.imag(h_est), \"--\")\n",
    "plt.xlabel(\"Subcarrier index\")\n",
    "plt.ylabel(\"Channel frequency response\")\n",
    "plt.legend([\"Ideal (real part)\", \"Ideal (imaginary part)\", \"Estimated (real part)\", \"Estimated (imaginary part)\"]);\n",
    "plt.title(\"Comparison of channel frequency responses\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c3b321-b6b8-40ab-9620-1e8d609ad267",
   "metadata": {},
   "source": [
    "### Understand the Difference Between the Channel Models\n",
    "Before we proceed with more advanced simulations, it is important to understand the differences\n",
    "between the UMi, UMa, and RMa models. In the following code snippet, we compute the empirical cummulative\n",
    "distribution function (CDF) of the condition number of the channel frequency response matrix\n",
    "between all receiver and transmit antennas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9833e89e-64ae-4724-a191-7241fed6bac4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def cond_hist(scenario):\n",
    "    \"\"\"Generates a histogram of the channel condition numbers\"\"\"\n",
    "    \n",
    "    # Setup a CIR generator\n",
    "    if scenario == \"umi\":\n",
    "        channel_model = UMi(carrier_frequency=carrier_frequency,\n",
    "                                      o2i_model=\"low\",\n",
    "                                      ut_array=ut_array,\n",
    "                                      bs_array=bs_array,\n",
    "                                      direction=\"uplink\",\n",
    "                                      enable_pathloss=False,\n",
    "                                      enable_shadow_fading=False)\n",
    "    elif scenario == \"uma\":\n",
    "        channel_model = UMa(carrier_frequency=carrier_frequency,\n",
    "                                      o2i_model=\"low\",\n",
    "                                      ut_array=ut_array,\n",
    "                                      bs_array=bs_array,\n",
    "                                      direction=\"uplink\",\n",
    "                                      enable_pathloss=False,\n",
    "                                      enable_shadow_fading=False)\n",
    "    elif scenario == \"rma\":\n",
    "        channel_model = RMa(carrier_frequency=carrier_frequency,\n",
    "                                      ut_array=ut_array,\n",
    "                                      bs_array=bs_array,\n",
    "                                      direction=\"uplink\",\n",
    "                                      enable_pathloss=False,\n",
    "                                      enable_shadow_fading=False)\n",
    "    \n",
    "    topology = gen_topology(1024, num_ut, scenario)\n",
    "\n",
    "    # Set the topology\n",
    "    channel_model.set_topology(*topology)\n",
    "    \n",
    "    # Generate random CIR realizations\n",
    "    # As we nned only a single sample in time, the sampling_frequency\n",
    "    # does not matter.\n",
    "    cir = channel_model(1, 1)\n",
    "    \n",
    "    # Compute the frequency response\n",
    "    h = cir_to_ofdm_channel(frequencies, *cir, normalize=True)\n",
    "    \n",
    "    h = tf.squeeze(h)\n",
    "    h = tf.transpose(h, [0,3,1,2])\n",
    "    \n",
    "    # Compute condition number\n",
    "    c = np.reshape(np.linalg.cond(h), [-1])\n",
    "    \n",
    "    # Compute normalized histogram\n",
    "    hist, bins = np.histogram(c, 100, (1, 100))\n",
    "    hist = hist/np.sum(hist)\n",
    "    return bins[:-1], hist\n",
    "\n",
    "plt.figure()\n",
    "for cdl_model in [\"umi\", \"uma\", \"rma\"]:\n",
    "    bins, hist = cond_hist(cdl_model)\n",
    "    plt.plot(bins, np.cumsum(hist))\n",
    "plt.xlim([0,40])\n",
    "plt.legend([\"UMi\", \"UMa\", \"RMa\"]);\n",
    "plt.xlabel(\"Channel Condition Number\")\n",
    "plt.ylabel(\"CDF\")\n",
    "plt.title(\"CDF of the channel condition number\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "535a6770-7857-4b28-9605-2f7699a5c6ad",
   "metadata": {},
   "source": [
    "From the figure above, you can observe that the UMi and UMa models\n",
    "are substantially better conditioned than the RMa models. This makes \n",
    "them more suitable for MIMO transmissions as we will observe in the next\n",
    "section.\n",
    "\n",
    "It is also interesting to look at the channel frequency responses of these different models, as done in the next cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5d9d6c37-b96d-4967-804e-ec628209ba58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def freq_response(scenario):\n",
    "    \"\"\"Generates an example frequency response\"\"\"\n",
    "    tf.random.set_seed(2)\n",
    "    \n",
    "    # Setup a CIR generator\n",
    "    if scenario == \"umi\":\n",
    "        channel_model = UMi(carrier_frequency=carrier_frequency,\n",
    "                                      o2i_model=\"low\",\n",
    "                                      ut_array=ut_array,\n",
    "                                      bs_array=bs_array,\n",
    "                                      direction=\"uplink\",\n",
    "                                      enable_pathloss=False,\n",
    "                                      enable_shadow_fading=False)\n",
    "    elif scenario == \"uma\":\n",
    "        channel_model = UMa(carrier_frequency=carrier_frequency,\n",
    "                                      o2i_model=\"low\",\n",
    "                                      ut_array=ut_array,\n",
    "                                      bs_array=bs_array,\n",
    "                                      direction=\"uplink\",\n",
    "                                      enable_pathloss=False,\n",
    "                                      enable_shadow_fading=False)\n",
    "    elif scenario == \"rma\":\n",
    "        channel_model = RMa(carrier_frequency=carrier_frequency,\n",
    "                                      ut_array=ut_array,\n",
    "                                      bs_array=bs_array,\n",
    "                                      direction=\"uplink\",\n",
    "                                      enable_pathloss=False,\n",
    "                                      enable_shadow_fading=False)\n",
    "    \n",
    "    topology = gen_topology(1, num_ut, scenario)\n",
    "\n",
    "    # Set the topology\n",
    "    channel_model.set_topology(*topology)\n",
    "    \n",
    "    # Generate random CIR realizations\n",
    "    # As we nned only a single sample in time, the sampling_frequency\n",
    "    # does not matter.\n",
    "    cir = channel_model(1, 1)\n",
    "    \n",
    "    # Compute the frequency response\n",
    "    h = cir_to_ofdm_channel(frequencies, *cir, normalize=True)\n",
    "    h = tf.squeeze(h)\n",
    "\n",
    "    return h[0,0]\n",
    "\n",
    "plt.figure()\n",
    "for cdl_model in [\"umi\", \"uma\", \"rma\"]:\n",
    "    h = freq_response(cdl_model)\n",
    "    plt.plot(np.real(h))\n",
    "plt.legend([\"UMi\", \"UMa\", \"RMa\"]);\n",
    "plt.xlabel(\"Subcarrier Index\")\n",
    "plt.ylabel(r\"$\\Re(h)$\")\n",
    "plt.title(\"Channel frequency response\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b6b276-cc49-4c34-b230-da335fbe57c6",
   "metadata": {},
   "source": [
    "The RMa model has significantly less frequency selectivity than the other models which makes channel estimation easier."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c51efaff-c18c-4f26-87a4-e5a7b51b11ec",
   "metadata": {},
   "source": [
    "### Setup a Keras Model for BER simulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c9b30ed0-ad9e-4e47-8321-11a4a7c165a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need to enable sionna.config.xla_compat before we can use\n",
    "# tf.function with jit_compile=True.\n",
    "# See https://nvlabs.github.io/sionna/api/config.html#sionna.Config.xla_compat\n",
    "sionna.config.xla_compat=True\n",
    "\n",
    "class Model(tf.keras.Model):\n",
    "    \"\"\"Simulate OFDM MIMO transmissions over a 3GPP 38.901 model.\n",
    "    \"\"\"\n",
    "    def __init__(self, scenario, perfect_csi):\n",
    "        super().__init__()\n",
    "        self._scenario = scenario\n",
    "        self._perfect_csi = perfect_csi\n",
    "        \n",
    "        # Internally set parameters\n",
    "        self._carrier_frequency = 3.5e9\n",
    "        self._fft_size = 128\n",
    "        self._subcarrier_spacing = 30e3\n",
    "        self._num_ofdm_symbols = 14\n",
    "        self._cyclic_prefix_length = 20\n",
    "        self._pilot_ofdm_symbol_indices = [2, 11]      \n",
    "        self._num_bs_ant = 8\n",
    "        self._num_ut = 4\n",
    "        self._num_ut_ant = 1\n",
    "        self._num_bits_per_symbol = 2 \n",
    "        self._coderate = 0.5\n",
    "    \n",
    "        # Create an RX-TX association matrix\n",
    "        # rx_tx_association[i,j]=1 means that receiver i gets at least one stream\n",
    "        # from transmitter j. Depending on the transmission direction (uplink or downlink),\n",
    "        # the role of UT and BS can change. \n",
    "        bs_ut_association = np.zeros([1, self._num_ut])\n",
    "        bs_ut_association[0, :] = 1\n",
    "        self._rx_tx_association = bs_ut_association\n",
    "        self._num_tx = self._num_ut\n",
    "        self._num_streams_per_tx = self._num_ut_ant\n",
    "\n",
    "            \n",
    "        # Setup an OFDM Resource Grid\n",
    "        self._rg = ResourceGrid(num_ofdm_symbols=self._num_ofdm_symbols,\n",
    "                                fft_size=self._fft_size,\n",
    "                                subcarrier_spacing=self._subcarrier_spacing,\n",
    "                                num_tx=self._num_tx,\n",
    "                                num_streams_per_tx=self._num_streams_per_tx,\n",
    "                                cyclic_prefix_length=self._cyclic_prefix_length,\n",
    "                                pilot_pattern=\"kronecker\",\n",
    "                                pilot_ofdm_symbol_indices=self._pilot_ofdm_symbol_indices)\n",
    "             \n",
    "        # Setup StreamManagement\n",
    "        self._sm = StreamManagement(self._rx_tx_association, self._num_streams_per_tx)\n",
    "               \n",
    "        # Configure antenna arrays\n",
    "        self._ut_array = AntennaArray(\n",
    "                                 num_rows=1,\n",
    "                                 num_cols=1,\n",
    "                                 polarization=\"single\",\n",
    "                                 polarization_type=\"V\",\n",
    "                                 antenna_pattern=\"omni\",\n",
    "                                 carrier_frequency=self._carrier_frequency)\n",
    "        \n",
    "        self._bs_array = AntennaArray(num_rows=1,\n",
    "                                      num_cols=int(self._num_bs_ant/2),\n",
    "                                      polarization=\"dual\",\n",
    "                                      polarization_type=\"cross\",\n",
    "                                      antenna_pattern=\"38.901\",\n",
    "                                      carrier_frequency=self._carrier_frequency)\n",
    "        \n",
    "        # Configure the channel model\n",
    "        if self._scenario == \"umi\":\n",
    "            self._channel_model = UMi(carrier_frequency=self._carrier_frequency,\n",
    "                                      o2i_model=\"low\",\n",
    "                                      ut_array=self._ut_array,\n",
    "                                      bs_array=self._bs_array,\n",
    "                                      direction=\"uplink\",\n",
    "                                      enable_pathloss=False,\n",
    "                                      enable_shadow_fading=False)\n",
    "        elif self._scenario == \"uma\":\n",
    "            self._channel_model = UMa(carrier_frequency=self._carrier_frequency,\n",
    "                                      o2i_model=\"low\",\n",
    "                                      ut_array=self._ut_array,\n",
    "                                      bs_array=self._bs_array,\n",
    "                                      direction=\"uplink\",\n",
    "                                      enable_pathloss=False,\n",
    "                                      enable_shadow_fading=False)\n",
    "        elif self._scenario == \"rma\":\n",
    "            self._channel_model = RMa(carrier_frequency=self._carrier_frequency,\n",
    "                                      ut_array=self._ut_array,\n",
    "                                      bs_array=self._bs_array,\n",
    "                                      direction=\"uplink\",\n",
    "                                      enable_pathloss=False,\n",
    "                                      enable_shadow_fading=False)\n",
    "            \n",
    "        # Instantiate other building blocks\n",
    "        self._binary_source = BinarySource()\n",
    "        self._qam_source = QAMSource(self._num_bits_per_symbol)\n",
    "        \n",
    "        self._n = int(self._rg.num_data_symbols*self._num_bits_per_symbol) # Number of coded bits\n",
    "        self._k = int(self._n*self._coderate)                              # Number of information bits\n",
    "        self._encoder = LDPC5GEncoder(self._k, self._n)\n",
    "        self._decoder = LDPC5GDecoder(self._encoder)\n",
    "        self._mapper = Mapper(\"qam\", self._num_bits_per_symbol)\n",
    "        self._rg_mapper = ResourceGridMapper(self._rg)\n",
    "        \n",
    "        self._ofdm_channel = OFDMChannel(self._channel_model, self._rg, add_awgn=True,\n",
    "                                         normalize_channel=True, return_channel=True)\n",
    "       \n",
    "        self._remove_nulled_subcarriers = RemoveNulledSubcarriers(self._rg)\n",
    "        self._ls_est = LSChannelEstimator(self._rg, interpolation_type=\"nn\")\n",
    "        self._lmmse_equ = LMMSEEqualizer(self._rg, self._sm)\n",
    "        self._demapper = Demapper(\"app\", \"qam\", self._num_bits_per_symbol)\n",
    "\n",
    "    def new_topology(self, batch_size):\n",
    "        \"\"\"Set new topology\"\"\"\n",
    "        topology = gen_topology(batch_size,\n",
    "                                self._num_ut,\n",
    "                                self._scenario,\n",
    "                                min_ut_velocity=0.0,\n",
    "                                max_ut_velocity=0.0)\n",
    "        \n",
    "        self._channel_model.set_topology(*topology)\n",
    "        \n",
    "    @tf.function(jit_compile=True)\n",
    "    def call(self, batch_size, ebno_db):        \n",
    "        self.new_topology(batch_size) \n",
    "        no = ebnodb2no(ebno_db, self._num_bits_per_symbol, self._coderate, self._rg)\n",
    "        b = self._binary_source([batch_size, self._num_tx, self._num_streams_per_tx, self._k])\n",
    "        c = self._encoder(b)\n",
    "        x = self._mapper(c)\n",
    "        x_rg = self._rg_mapper(x)   \n",
    "        y, h = self._ofdm_channel([x_rg, no])\n",
    "        if self._perfect_csi:\n",
    "            h_hat = self._remove_nulled_subcarriers(h)\n",
    "            err_var = 0.0        \n",
    "        else:\n",
    "            h_hat, err_var = self._ls_est ([y, no])\n",
    "        x_hat, no_eff = self._lmmse_equ([y, h_hat, err_var, no])\n",
    "        llr = self._demapper([x_hat, no_eff])\n",
    "        b_hat = self._decoder(llr)                       \n",
    "        return b, b_hat "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fc8be0d-96fb-4c15-9234-0c13efa1250a",
   "metadata": {},
   "source": [
    "If you do not want to run the simulations (which can take quite some time) yourself, you can skip the next cell and simply visualize the results in the next cell. The reason why we simulate for a rather large number of ``max_mc_iter``, is that for every batch, a new topology (i.e., user drop with new large-scale parameters) is generated. Our goal is here to average over many such topologies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fbe0e7ab-69fb-4f23-9dd9-23773a6c6655",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -5.0 | 7.1096e-02 | 5.0537e-01 |      223649 |     3145728 |         1035 |        2048 |        27.9 |reached target block errors\n",
      "     -4.0 | 3.9041e-02 | 2.6855e-01 |      245625 |     6291456 |         1100 |        4096 |         0.4 |reached target block errors\n",
      "     -3.0 | 2.1455e-02 | 1.3979e-01 |      236218 |    11010048 |         1002 |        7168 |         0.7 |reached target block errors\n",
      "     -2.0 | 1.1953e-02 | 7.8281e-02 |      235015 |    19660800 |         1002 |       12800 |         1.3 |reached target block errors\n",
      "     -1.0 | 6.6370e-03 | 4.1260e-02 |      250538 |    37748736 |         1014 |       24576 |         2.5 |reached target block errors\n",
      "      0.0 | 3.9849e-03 | 2.3321e-02 |      263243 |    66060288 |         1003 |       43008 |         4.4 |reached target block errors\n",
      "      1.0 | 2.4968e-03 | 1.5171e-02 |      253302 |   101449728 |         1002 |       66048 |         6.7 |reached target block errors\n",
      "      2.0 | 1.5606e-03 | 9.0965e-03 |      265100 |   169869312 |         1006 |      110592 |        11.3 |reached target block errors\n",
      "      3.0 | 9.8858e-04 | 5.6669e-03 |      268220 |   271319040 |         1001 |      176640 |        18.1 |reached target block errors\n",
      "      4.0 | 5.9893e-04 | 3.5849e-03 |      257647 |   430178304 |         1004 |      280064 |        28.7 |reached target block errors\n",
      "      5.0 | 3.7780e-04 | 2.2014e-03 |      264134 |   699138048 |         1002 |      455168 |        46.8 |reached target block errors\n",
      "      6.0 | 2.2261e-04 | 1.2910e-03 |      175069 |   786432000 |          661 |      512000 |        52.8 |reached max iter       \n",
      "      7.0 | 1.4676e-04 | 8.3203e-04 |      115414 |   786432000 |          426 |      512000 |        52.8 |reached max iter       \n",
      "      8.0 | 8.2901e-05 | 4.9609e-04 |       65196 |   786432000 |          254 |      512000 |        52.8 |reached max iter       \n",
      "      9.0 | 6.4590e-05 | 3.8477e-04 |       50796 |   786432000 |          197 |      512000 |        52.6 |reached max iter       \n",
      "     10.0 | 3.7591e-05 | 2.5000e-04 |       29563 |   786432000 |          128 |      512000 |        52.7 |reached max iter       \n",
      "     11.0 | 2.3387e-05 | 1.5820e-04 |       18392 |   786432000 |           81 |      512000 |        52.6 |reached max iter       \n",
      "     12.0 | 1.9283e-05 | 1.2305e-04 |       15165 |   786432000 |           63 |      512000 |        52.6 |reached max iter       \n",
      "     13.0 | 8.9823e-06 | 7.0312e-05 |        7064 |   786432000 |           36 |      512000 |        52.6 |reached max iter       \n",
      "     14.0 | 5.1371e-06 | 5.2734e-05 |        4040 |   786432000 |           27 |      512000 |        52.6 |reached max iter       \n",
      "     15.0 | 2.2189e-06 | 2.5391e-05 |        1745 |   786432000 |           13 |      512000 |        52.6 |reached max iter       \n",
      "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU.\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -5.0 | 2.9643e-01 | 1.0000e+00 |      466249 |     1572864 |         1024 |        1024 |        21.3 |reached target block errors\n",
      "     -4.0 | 2.6703e-01 | 1.0000e+00 |      420009 |     1572864 |         1024 |        1024 |         0.1 |reached target block errors\n",
      "     -3.0 | 2.2791e-01 | 9.9609e-01 |      358464 |     1572864 |         1020 |        1024 |         0.1 |reached target block errors\n",
      "     -2.0 | 1.6929e-01 | 8.5156e-01 |      399413 |     2359296 |         1308 |        1536 |         0.2 |reached target block errors\n",
      "     -1.0 | 1.1276e-01 | 5.9424e-01 |      354712 |     3145728 |         1217 |        2048 |         0.2 |reached target block errors\n",
      "      0.0 | 7.0166e-02 | 3.7630e-01 |      331083 |     4718592 |         1156 |        3072 |         0.3 |reached target block errors\n",
      "      1.0 | 3.9429e-02 | 2.1723e-01 |      279074 |     7077888 |         1001 |        4608 |         0.5 |reached target block errors\n",
      "      2.0 | 2.5046e-02 | 1.4089e-01 |      295455 |    11796480 |         1082 |        7680 |         0.8 |reached target block errors\n",
      "      3.0 | 1.6036e-02 | 8.6362e-02 |      290063 |    18087936 |         1017 |       11776 |         1.2 |reached target block errors\n",
      "      4.0 | 1.0529e-02 | 5.5971e-02 |      289820 |    27525120 |         1003 |       17920 |         1.9 |reached target block errors\n",
      "      5.0 | 7.5292e-03 | 3.8274e-02 |      307901 |    40894464 |         1019 |       26624 |         2.8 |reached target block errors\n",
      "      6.0 | 5.1773e-03 | 2.5930e-02 |      309443 |    59768832 |         1009 |       38912 |         4.0 |reached target block errors\n",
      "      7.0 | 3.6257e-03 | 1.8008e-02 |      310801 |    85721088 |         1005 |       55808 |         5.8 |reached target block errors\n",
      "      8.0 | 2.6579e-03 | 1.3781e-02 |      298907 |   112459776 |         1009 |       73216 |         7.6 |reached target block errors\n",
      "      9.0 | 2.2009e-03 | 1.1557e-02 |      292515 |   132907008 |         1000 |       86528 |         9.0 |reached target block errors\n",
      "     10.0 | 1.6736e-03 | 9.7851e-03 |      264557 |   158072832 |         1007 |      102912 |        10.7 |reached target block errors\n",
      "     11.0 | 1.3004e-03 | 8.8067e-03 |      227037 |   174587904 |         1001 |      113664 |        11.8 |reached target block errors\n",
      "     12.0 | 1.2042e-03 | 9.6689e-03 |      191298 |   158859264 |         1000 |      103424 |        10.8 |reached target block errors\n",
      "     13.0 | 1.1146e-03 | 1.0589e-02 |      162159 |   145489920 |         1003 |       94720 |         9.8 |reached target block errors\n",
      "     14.0 | 1.0679e-03 | 1.2168e-02 |      135214 |   126615552 |         1003 |       82432 |         8.6 |reached target block errors\n",
      "     15.0 | 1.1502e-03 | 1.4826e-02 |      119396 |   103809024 |         1002 |       67584 |         7.0 |reached target block errors\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -5.0 | 7.2186e-02 | 5.1758e-01 |      227077 |     3145728 |         1060 |        2048 |        21.7 |reached target block errors\n",
      "     -4.0 | 3.4300e-02 | 2.4609e-01 |      242769 |     7077888 |         1134 |        4608 |         0.5 |reached target block errors\n",
      "     -3.0 | 1.6501e-02 | 1.2017e-01 |      220608 |    13369344 |         1046 |        8704 |         0.9 |reached target block errors\n",
      "     -2.0 | 8.1806e-03 | 5.9837e-02 |      212304 |    25952256 |         1011 |       16896 |         1.7 |reached target block errors\n",
      "     -1.0 | 4.3676e-03 | 3.0048e-02 |      223264 |    51118080 |         1000 |       33280 |         3.4 |reached target block errors\n",
      "      0.0 | 2.6359e-03 | 1.7006e-02 |      240466 |    91226112 |         1010 |       59392 |         6.1 |reached target block errors\n",
      "      1.0 | 1.3907e-03 | 8.9862e-03 |      238431 |   171442176 |         1003 |      111616 |        11.5 |reached target block errors\n",
      "      2.0 | 8.0677e-04 | 5.0052e-03 |      248078 |   307494912 |         1002 |      200192 |        20.7 |reached target block errors\n",
      "      3.0 | 4.0438e-04 | 2.5365e-03 |      244874 |   605552640 |         1000 |      394240 |        40.9 |reached target block errors\n",
      "      4.0 | 2.1496e-04 | 1.3184e-03 |      169054 |   786432000 |          675 |      512000 |        53.1 |reached max iter       \n",
      "      5.0 | 1.2614e-04 | 8.0664e-04 |       99201 |   786432000 |          413 |      512000 |        53.0 |reached max iter       \n",
      "      6.0 | 6.7013e-05 | 4.1016e-04 |       52701 |   786432000 |          210 |      512000 |        53.0 |reached max iter       \n",
      "      7.0 | 3.5152e-05 | 2.0508e-04 |       27645 |   786432000 |          105 |      512000 |        52.9 |reached max iter       \n",
      "      8.0 | 2.0339e-05 | 1.3477e-04 |       15995 |   786432000 |           69 |      512000 |        52.9 |reached max iter       \n",
      "      9.0 | 1.2319e-05 | 6.4453e-05 |        9688 |   786432000 |           33 |      512000 |        52.9 |reached max iter       \n",
      "     10.0 | 7.5582e-06 | 5.2734e-05 |        5944 |   786432000 |           27 |      512000 |        53.0 |reached max iter       \n",
      "     11.0 | 3.7626e-06 | 2.9297e-05 |        2959 |   786432000 |           15 |      512000 |        53.0 |reached max iter       \n",
      "     12.0 | 1.7904e-06 | 2.1484e-05 |        1408 |   786432000 |           11 |      512000 |        53.0 |reached max iter       \n",
      "     13.0 | 1.1584e-06 | 1.1719e-05 |         911 |   786432000 |            6 |      512000 |        53.0 |reached max iter       \n",
      "     14.0 | 7.2988e-07 | 3.9063e-06 |         574 |   786432000 |            2 |      512000 |        53.0 |reached max iter       \n",
      "     15.0 | 1.0351e-06 | 5.8594e-06 |         814 |   786432000 |            3 |      512000 |        53.0 |reached max iter       \n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -5.0 | 2.9996e-01 | 1.0000e+00 |      471791 |     1572864 |         1024 |        1024 |        21.1 |reached target block errors\n",
      "     -4.0 | 2.7072e-01 | 1.0000e+00 |      425811 |     1572864 |         1024 |        1024 |         0.1 |reached target block errors\n",
      "     -3.0 | 2.3427e-01 | 1.0000e+00 |      368467 |     1572864 |         1024 |        1024 |         0.1 |reached target block errors\n",
      "     -2.0 | 1.7587e-01 | 8.7174e-01 |      414940 |     2359296 |         1339 |        1536 |         0.2 |reached target block errors\n",
      "     -1.0 | 1.2033e-01 | 6.3867e-01 |      378535 |     3145728 |         1308 |        2048 |         0.2 |reached target block errors\n",
      "      0.0 | 6.9556e-02 | 3.9141e-01 |      273507 |     3932160 |         1002 |        2560 |         0.3 |reached target block errors\n",
      "      1.0 | 4.4011e-02 | 2.4132e-01 |      311505 |     7077888 |         1112 |        4608 |         0.5 |reached target block errors\n",
      "      2.0 | 2.8742e-02 | 1.5415e-01 |      293844 |    10223616 |         1026 |        6656 |         0.7 |reached target block errors\n",
      "      3.0 | 1.7114e-02 | 9.0376e-02 |      296091 |    17301504 |         1018 |       11264 |         1.1 |reached target block errors\n",
      "      4.0 | 1.0157e-02 | 5.1282e-02 |      311521 |    30670848 |         1024 |       19968 |         2.0 |reached target block errors\n",
      "      5.0 | 8.0138e-03 | 3.9414e-02 |      315114 |    39321600 |         1009 |       25600 |         2.6 |reached target block errors\n",
      "      6.0 | 5.7654e-03 | 2.7103e-02 |      330990 |    57409536 |         1013 |       37376 |         3.8 |reached target block errors\n",
      "      7.0 | 4.3783e-03 | 2.0406e-02 |      330547 |    75497472 |         1003 |       49152 |         5.0 |reached target block errors\n",
      "      8.0 | 3.5508e-03 | 1.6734e-02 |      329507 |    92798976 |         1011 |       60416 |         6.1 |reached target block errors\n",
      "      9.0 | 3.0306e-03 | 1.5171e-02 |      307449 |   101449728 |         1002 |       66048 |         6.7 |reached target block errors\n",
      "     10.0 | 2.6278e-03 | 1.4482e-02 |      278990 |   106168320 |         1001 |       69120 |         7.0 |reached target block errors\n",
      "     11.0 | 2.3524e-03 | 1.5159e-02 |      240503 |   102236160 |         1009 |       66560 |         6.7 |reached target block errors\n",
      "     12.0 | 2.5297e-03 | 1.8139e-02 |      214857 |    84934656 |         1003 |       55296 |         5.6 |reached target block errors\n",
      "     13.0 | 2.3190e-03 | 2.1918e-02 |      164137 |    70778880 |         1010 |       46080 |         4.7 |reached target block errors\n",
      "     14.0 | 2.2989e-03 | 2.5619e-02 |      139211 |    60555264 |         1010 |       39424 |         4.0 |reached target block errors\n",
      "     15.0 | 2.2612e-03 | 2.8674e-02 |      122701 |    54263808 |         1013 |       35328 |         3.6 |reached target block errors\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -5.0 | 1.1218e-01 | 6.0400e-01 |      352900 |     3145728 |         1237 |        2048 |        20.2 |reached target block errors\n",
      "     -4.0 | 7.6053e-02 | 4.0469e-01 |      299052 |     3932160 |         1036 |        2560 |         0.3 |reached target block errors\n",
      "     -3.0 | 6.6383e-02 | 3.2878e-01 |      313232 |     4718592 |         1010 |        3072 |         0.3 |reached target block errors\n",
      "     -2.0 | 5.0129e-02 | 2.5195e-01 |      315385 |     6291456 |         1032 |        4096 |         0.4 |reached target block errors\n",
      "     -1.0 | 3.3127e-02 | 1.7057e-01 |      312623 |     9437184 |         1048 |        6144 |         0.6 |reached target block errors\n",
      "      0.0 | 2.7713e-02 | 1.3984e-01 |      326920 |    11796480 |         1074 |        7680 |         0.8 |reached target block errors\n",
      "      1.0 | 1.9857e-02 | 9.7656e-02 |      312320 |    15728640 |         1000 |       10240 |         1.0 |reached target block errors\n",
      "      2.0 | 1.6031e-02 | 8.0078e-02 |      315191 |    19660800 |         1025 |       12800 |         1.3 |reached target block errors\n",
      "      3.0 | 1.1741e-02 | 5.7904e-02 |      313944 |    26738688 |         1008 |       17408 |         1.7 |reached target block errors\n",
      "      4.0 | 9.4707e-03 | 4.6596e-02 |      312820 |    33030144 |         1002 |       21504 |         2.1 |reached target block errors\n",
      "      5.0 | 7.3535e-03 | 3.6350e-02 |      312285 |    42467328 |         1005 |       27648 |         2.7 |reached target block errors\n",
      "      6.0 | 5.7442e-03 | 2.8292e-02 |      316219 |    55050240 |         1014 |       35840 |         3.6 |reached target block errors\n",
      "      7.0 | 4.3868e-03 | 2.2978e-02 |      293242 |    66846720 |         1000 |       43520 |         4.3 |reached target block errors\n",
      "      8.0 | 3.4328e-03 | 1.7235e-02 |      307761 |    89653248 |         1006 |       58368 |         5.8 |reached target block errors\n",
      "      9.0 | 2.7156e-03 | 1.3672e-02 |      305395 |   112459776 |         1001 |       73216 |         7.3 |reached target block errors\n",
      "     10.0 | 2.0763e-03 | 1.1172e-02 |      285749 |   137625600 |         1001 |       89600 |         8.9 |reached target block errors\n",
      "     11.0 | 1.5832e-03 | 8.3993e-03 |      290097 |   183238656 |         1002 |      119296 |        11.9 |reached target block errors\n",
      "     12.0 | 1.1819e-03 | 6.4524e-03 |      281639 |   238288896 |         1001 |      155136 |        15.4 |reached target block errors\n",
      "     13.0 | 9.3162e-04 | 4.9874e-03 |      287202 |   308281344 |         1001 |      200704 |        20.0 |reached target block errors\n",
      "     14.0 | 7.0943e-04 | 3.9981e-03 |      272823 |   384565248 |         1001 |      250368 |        24.9 |reached target block errors\n",
      "     15.0 | 5.7079e-04 | 3.2230e-03 |      272025 |   476577792 |         1000 |      310272 |        30.9 |reached target block errors\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -5.0 | 2.9893e-01 | 1.0000e+00 |      470184 |     1572864 |         1024 |        1024 |        21.0 |reached target block errors\n",
      "     -4.0 | 2.7396e-01 | 1.0000e+00 |      430909 |     1572864 |         1024 |        1024 |         0.1 |reached target block errors\n",
      "     -3.0 | 2.2892e-01 | 9.8145e-01 |      360059 |     1572864 |         1005 |        1024 |         0.1 |reached target block errors\n",
      "     -2.0 | 1.7590e-01 | 7.8841e-01 |      414991 |     2359296 |         1211 |        1536 |         0.2 |reached target block errors\n",
      "     -1.0 | 1.2533e-01 | 5.8057e-01 |      394240 |     3145728 |         1189 |        2048 |         0.2 |reached target block errors\n",
      "      0.0 | 1.0202e-01 | 4.6133e-01 |      401178 |     3932160 |         1181 |        2560 |         0.3 |reached target block errors\n",
      "      1.0 | 7.9942e-02 | 3.6165e-01 |      377212 |     4718592 |         1111 |        3072 |         0.3 |reached target block errors\n",
      "      2.0 | 6.4012e-02 | 2.9492e-01 |      352387 |     5505024 |         1057 |        3584 |         0.4 |reached target block errors\n",
      "      3.0 | 5.1941e-02 | 2.3698e-01 |      367634 |     7077888 |         1092 |        4608 |         0.5 |reached target block errors\n",
      "      4.0 | 3.8278e-02 | 1.7464e-01 |      361239 |     9437184 |         1073 |        6144 |         0.6 |reached target block errors\n",
      "      5.0 | 2.9897e-02 | 1.3503e-01 |      352682 |    11796480 |         1037 |        7680 |         0.8 |reached target block errors\n",
      "      6.0 | 2.4803e-02 | 1.1306e-01 |      351106 |    14155776 |         1042 |        9216 |         0.9 |reached target block errors\n",
      "      7.0 | 1.8750e-02 | 8.5003e-02 |      339141 |    18087936 |         1001 |       11776 |         1.2 |reached target block errors\n",
      "      8.0 | 1.4703e-02 | 6.7619e-02 |      335327 |    22806528 |         1004 |       14848 |         1.5 |reached target block errors\n",
      "      9.0 | 1.1462e-02 | 5.3262e-02 |      333508 |    29097984 |         1009 |       18944 |         1.9 |reached target block errors\n",
      "     10.0 | 8.3913e-03 | 3.9570e-02 |      329960 |    39321600 |         1013 |       25600 |         2.5 |reached target block errors\n",
      "     11.0 | 7.3333e-03 | 3.3766e-02 |      340262 |    46399488 |         1020 |       30208 |         3.0 |reached target block errors\n",
      "     12.0 | 5.8008e-03 | 2.7127e-02 |      328462 |    56623104 |         1000 |       36864 |         3.7 |reached target block errors\n",
      "     13.0 | 4.6824e-03 | 2.1832e-02 |      331412 |    70778880 |         1006 |       46080 |         4.6 |reached target block errors\n",
      "     14.0 | 3.4007e-03 | 1.6544e-02 |      318254 |    93585408 |         1008 |       60928 |         6.1 |reached target block errors\n",
      "     15.0 | 2.5160e-03 | 1.2143e-02 |      318566 |   126615552 |         1001 |       82432 |         8.2 |reached target block errors\n"
     ]
    }
   ],
   "source": [
    "SIMS = {\n",
    "    \"ebno_db\" : list(np.arange(-5, 16, 1.0)),\n",
    "    \"scenario\" : [\"umi\", \"uma\", \"rma\"],\n",
    "    \"perfect_csi\" : [True, False],\n",
    "    \"ber\" : [],\n",
    "    \"bler\" : [],\n",
    "    \"duration\" : None\n",
    "}\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "for scenario in SIMS[\"scenario\"]:\n",
    "    for perfect_csi in SIMS[\"perfect_csi\"]:\n",
    "    \n",
    "        model = Model(scenario=scenario,\n",
    "                      perfect_csi=perfect_csi)\n",
    "    \n",
    "        ber, bler = sim_ber(model,\n",
    "                            SIMS[\"ebno_db\"],\n",
    "                            batch_size=128,\n",
    "                            max_mc_iter=1000,\n",
    "                            num_target_block_errors=1000)\n",
    "    \n",
    "        SIMS[\"ber\"].append(list(ber.numpy()))\n",
    "        SIMS[\"bler\"].append(list(bler.numpy()))\n",
    "    \n",
    "SIMS[\"duration\"] = time.time() -  start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7c0427ed-c7fd-4593-9cdb-13d8876b261f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load results (uncomment to show saved results from the cell above)\n",
    "#SIMS = eval(\"{'ebno_db': [-5.0, -4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0], 'scenario': ['umi', 'uma', 'rma'], 'perfect_csi': [True, False], 'ber': [[0.07905292510986328, 0.03808736801147461, 0.017681360244750977, 0.009239894087596606, 0.0050665537516276045, 0.0027629886053304755, 0.0016827532040175571, 0.0008500541736877042, 0.0004983015045593167, 0.00031632105509440104, 0.00018594996134440104, 0.00010455576578776041, 6.090927124023438e-05, 3.9520263671875e-05, 2.5684356689453124e-05, 1.4940897623697916e-05, 7.539113362630208e-06, 4.683176676432292e-06, 3.59344482421875e-06, 1.5436808268229167e-06, 7.578531901041666e-07], [0.29719797770182294, 0.26843706766764325, 0.2296581268310547, 0.17483605278862846, 0.10778331756591797, 0.07155566745334202, 0.04232830471462674, 0.022064606348673504, 0.015027618408203125, 0.008189432548754143, 0.005684130119554924, 0.00370962642928929, 0.002616008663393743, 0.0019156403011745876, 0.0015677095994417248, 0.0013081868489583333, 0.0010344430083840252, 0.0010432377567997685, 0.0009155009900555915, 0.0009102860117346291, 0.0008864811488560267], [0.06746260325113933, 0.0329127311706543, 0.014757650869864004, 0.007593437477394387, 0.003813561333550347, 0.0018911941496331237, 0.001028917273696588, 0.000513251788663255, 0.0002842496236165365, 0.00016032155354817708, 8.596547444661459e-05, 4.677454630533854e-05, 2.0359039306640624e-05, 1.1446634928385416e-05, 1.0133107503255209e-05, 3.92913818359375e-07, 1.682281494140625e-06, 6.421407063802083e-07, 1.3987223307291666e-08, 4.895528157552083e-07, 1.2715657552083333e-09], [0.2960662841796875, 0.2712268829345703, 0.2315998077392578, 0.17950481838650173, 0.11626561482747395, 0.0681504143608941, 0.04071949146412037, 0.02562223161969866, 0.014265790397738233, 0.009787991515591614, 0.006755871242947049, 0.004930473776424632, 0.003845776165569867, 0.003375189644949777, 0.0026965757616523173, 0.002434003298685431, 0.002402254330214634, 0.0021742226420969203, 0.0020746665425819925, 0.0021730139552350023, 0.0022606077648344492], [0.09145228068033855, 0.06702995300292969, 0.05034939448038737, 0.03308937766335227, 0.024936834971110027, 0.018199747258966618, 0.014243278974368249, 0.010329062478584155, 0.00815982288784451, 0.006009458884214744, 0.004231770833333333, 0.0033478243597622574, 0.0025576324126052015, 0.0019759736530521373, 0.0014438384618514623, 0.001123528113731971, 0.0008716690492438504, 0.0006736387408842243, 0.0004936694871486797, 0.00040878831294544955, 0.0002836583455403646], [0.2979132334391276, 0.2661628723144531, 0.22218640645345053, 0.1630952623155382, 0.11636797587076823, 0.08418807983398438, 0.06609598795572917, 0.047115184642650465, 0.035385449727376304, 0.026970704396565754, 0.02249379743609512, 0.016286409818209134, 0.011899021693638392, 0.008838085418051861, 0.007266274813948007, 0.005744590415610923, 0.0042660244551720895, 0.003129789240790991, 0.002527833716269651, 0.002067384265718006, 0.0014839694274598686]], 'bler': [[0.53173828125, 0.26806640625, 0.1279296875, 0.0637600806451613, 0.03380926724137931, 0.017422566371681415, 0.010500672043010752, 0.00547640931372549, 0.0030405569595645414, 0.001916015625, 0.0010703125, 0.00063671875, 0.00039453125, 0.000236328125, 0.000154296875, 9.1796875e-05, 6.25e-05, 3.515625e-05, 2.5390625e-05, 1.5625e-05, 1.171875e-05], [1.0, 1.0, 0.994140625, 0.86328125, 0.59619140625, 0.392578125, 0.2348090277777778, 0.127197265625, 0.081484375, 0.04469992897727273, 0.03009588068181818, 0.019152002427184466, 0.01331313775510204, 0.01019287109375, 0.008390893240343348, 0.00784375, 0.007462130248091603, 0.008697916666666666, 0.01016029792746114, 0.011945026676829269, 0.014048549107142858], [0.49609375, 0.25244140625, 0.10894097222222222, 0.05495876736111111, 0.026328125, 0.01235750786163522, 0.006649925595238095, 0.0034094621080139375, 0.001748046875, 0.0010078125, 0.000537109375, 0.000291015625, 0.000130859375, 9.1796875e-05, 6.0546875e-05, 9.765625e-06, 1.953125e-05, 1.3671875e-05, 5.859375e-06, 3.90625e-06, 1.953125e-06], [1.0, 1.0, 0.9951171875, 0.8912760416666666, 0.62353515625, 0.3834635416666667, 0.2265625, 0.14020647321428573, 0.07458043981481481, 0.050380608974358976, 0.03264973958333333, 0.023115808823529413, 0.017648507882882882, 0.015516493055555556, 0.013366284013605442, 0.013377568493150685, 0.015814012096774195, 0.017085597826086957, 0.0193958849009901, 0.02383753765060241, 0.02837611607142857], [0.5185546875, 0.3645833333333333, 0.267822265625, 0.17844460227272727, 0.128662109375, 0.09259588068181818, 0.07241030092592593, 0.05180921052631579, 0.041056315104166664, 0.030048076923076924, 0.021399456521739132, 0.016904633620689655, 0.013012210264900662, 0.01011981865284974, 0.007512019230769231, 0.0060276442307692305, 0.004715737951807229, 0.0035807291666666665, 0.0027969644134477824, 0.0022843567251461987, 0.001625], [1.0, 1.0, 0.9765625, 0.7649739583333334, 0.55517578125, 0.40625, 0.30747767857142855, 0.22200520833333334, 0.169921875, 0.1259765625, 0.10546875, 0.0751953125, 0.05613839285714286, 0.041555851063829786, 0.034078663793103446, 0.026551942567567568, 0.02025612113402062, 0.014954079198473283, 0.012090203220858896, 0.009994818239795918, 0.007302355410447761]], 'duration': 14960.869339227676}\")\n",
    "\n",
    "plt.figure()\n",
    "plt.xlabel(r\"$E_b/N_0$ (dB)\")\n",
    "plt.ylabel(\"BLER\")\n",
    "plt.grid(which=\"both\")\n",
    "\n",
    "i=0\n",
    "legend = []\n",
    "for scenario in SIMS[\"scenario\"]:\n",
    "    for perfect_csi in SIMS[\"perfect_csi\"]:\n",
    "        if scenario==\"umi\":\n",
    "            r = \"r\"\n",
    "            t = \"UMi\"\n",
    "        elif scenario==\"uma\":\n",
    "            r = \"b\"\n",
    "            t = \"UMa\"\n",
    "        else:\n",
    "            r = \"g\"\n",
    "            t = \"RMa\"\n",
    "        if perfect_csi:\n",
    "            r += \"-\"\n",
    "        else:\n",
    "            r += \"--\"\n",
    "                \n",
    "        plt.semilogy(SIMS[\"ebno_db\"], SIMS[\"bler\"][i], r);\n",
    "        s = \"{} - {} CSI\".format(t,\"perf.\" if perfect_csi else \"imperf.\")\n",
    "        \n",
    "        legend.append(s)\n",
    "        i += 1\n",
    "plt.legend(legend)\n",
    "plt.ylim([1e-3, 1])\n",
    "plt.title(\"Multiuser 4x8 MIMO Uplink over Different 3GPP 38.901 Models\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53a18c80-2bba-449a-b2c2-5e75ee2196f6",
   "metadata": {},
   "source": [
    "Due to the worse channel conditioning, the RMa model achieves the worst performance with perfect CSI. However, as a result of the smaller frequency selectivity, imperfect channel estimation only leads to a constant 5dB performace loss. For the UMI and UMa models, the used channel estimator with nearest-neighbor interpolation is not accurate enough so that the BER curves saturate at high SNR. This could, for example, be circumvented with another interpolation method (e.g., [linear interpolation with time averaging](https://nvlabs.github.io/sionna/api/ofdm.html#linearinterpolator)) or a different pilot pattern."
   ]
  }
 ],
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