examples/Introduction_to_Iterative_Detection_and_Decoding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Iterative Detection and Decoding\n",
    "In the following, we will present a discussion about the performance gains of iterative detection and decoding (IDD, first presented in [1]) with OFDM MIMO detection and soft-input soft-output (SISO) LDPC decoding. We will compare non-iterative soft-output LMMSE, K-Best and expectation propagation (EP) detection with iterative SISO MMSE-PIC detection [2].\n",
    "\n",
    "For the non-IDD models, the signal processing pipeline looks as follows:"
   ]
  },
  {
   "attachments": {
    "block_diagram.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![block_diagram.png](attachment:block_diagram.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterative Detection and Decoding\n",
    "The IDD MIMO receiver iteratively exchanges soft-information between the data detector and the channel decoder, which works the following:"
   ]
  },
  {
   "attachments": {
    "idd_diagram.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![idd_diagram.png](attachment:idd_diagram.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We denote $\\mathrm{L}^{D}$ as the intrinsic information (represented by log-likelihood ratios, LLRs) and $\\mathrm{L}^{E} = \\mathrm{L}^{D} - \\mathrm{L}^{A}$ as extrinsic information, which corresponds to the information gain in $\\mathrm{L}^{D}$ relative to the a-priori information $\\mathrm{L}^{A}$. The a-priori LLRs represent soft information, provided to either the input of the detector (i.e., $\\mathrm{L}^{A}_{Det}$) or the decoder (i.e., $\\mathrm{L}^{A}_{Dec}$). While exchanging extrinsic information is standard for classical IDD, the SISO MMSE-PIC detector [2] turned out to work better when provided with intrinsic a-priori information from the decoder.\n",
    "\n",
    "Originally, IDD was proposed with a resetting (Turbo) decoder [1]. However, state-of-the-art IDD with LDPC message passing decoding showed better performance with a non-resetting decoder [3], particularly for a low number of decoding iterations. Therefore, we will forward the decoder state (i.e., the check node to variable node messages) from each IDD iteration to the next."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "* [GPU configuration and imports](#GPU-Configuration-and-Imports)\n",
    "* [Simulation parameters](#Simulation-Parameters)\n",
    "* [Declaration of the non-IDD and IDD simulation models](#non-idd-and-idd-models)\n",
    "* [Non-IDD vs. IDD benchmarks](#non-idd-vs-idd-benchmarks)\n",
    "* [Discussion: Optimizing IDD with machine learning](#optimizing-idd-with-ml)\n",
    "* [List of comments](#list-of-comments)\n",
    "* [References](#references)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GPU configuration and imports <a class=\"anchor\" id=\"GPU-Configuration-and-Imports\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of GPUs available : 5\n",
      "Only GPU number 0 used.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import Model\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,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\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",
    "from sionna.utils import QAMSource, BinarySource, sim_ber, ebnodb2no, QAMSource, expand_to_rank\n",
    "from sionna.mapping import Mapper, Constellation\n",
    "from sionna.ofdm import ResourceGrid, ResourceGridMapper, LSChannelEstimator, LinearDetector, KBestDetector, EPDetector, \\\n",
    "    RemoveNulledSubcarriers, MMSEPICDetector\n",
    "from sionna.channel import GenerateOFDMChannel, OFDMChannel, RayleighBlockFading, gen_single_sector_topology\n",
    "from sionna.channel.tr38901 import UMa, Antenna, PanelArray\n",
    "from sionna.fec.ldpc import LDPC5GEncoder\n",
    "from sionna.fec.ldpc import LDPC5GDecoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation parameters <a class=\"anchor\" id=\"Simulation-Parameters\"></a>\n",
    "In the following, we set the simulation parameters. Please modify at will; adapting the batch size to your hardware setup might be beneficial.\n",
    "\n",
    "The standard configuration will implement a coded 5G-near MU-MIMO OFDM uplink transmission over 3GPP UMa channels, with 4 single-antenna UEs, 16-QAM mapping, and a 16 element dual-polarized uniform planar antenna array (UPA) at the gNB. We implement least squares channel estimation with linear interpolation. Alternatively, we implement iid Rayleigh fading channels and perfect channel state information (CSI), which can be controlled by the model parameter `perfect_csi_rayleigh`.\n",
    "As channel code, we will apply a rate-matched 5G LDPC code at rate 1/2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-17 17:01:19.630302: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-02-17 17:01:20.132372: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 74106 MB memory:  -> device: 0, name: A100-SXM4-80GB, pci bus id: 0000:01:00.0, compute capability: 8.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SIMPLE_SIM = False   # reduced simulation time for simple simulation if set to True\n",
    "if SIMPLE_SIM:\n",
    "    batch_size = int(1e1)  # number of OFDM frames to be analyzed per batch\n",
    "    num_iter = 5  # number of Monte Carlo Iterations (total number of Monte Carlo trials is num_iter*batch_size)\n",
    "    num_steps = 6\n",
    "    tf.config.run_functions_eagerly(True)   # run eagerly for better debugging\n",
    "else:\n",
    "    batch_size = int(64)  # number of OFDM frames to be analyzed per batch\n",
    "    num_iter = 128  # number of Monte Carlo Iterations (total number of Monte Carlo trials is num_iter*batch_size)\n",
    "    num_steps = 11\n",
    "\n",
    "ebno_db_min_perf_csi = -10  # min EbNo value in dB for perfect csi benchmarks\n",
    "ebno_db_max_perf_csi = 0\n",
    "ebno_db_min_cest = -10\n",
    "ebno_db_max_cest = 10\n",
    "\n",
    "\n",
    "NUM_OFDM_SYMBOLS = 14\n",
    "FFT_SIZE = 12*4 # 4 PRBs\n",
    "SUBCARRIER_SPACING = 30e3 # Hz\n",
    "CARRIER_FREQUENCY = 3.5e9 # Hz\n",
    "SPEED = 3. # m/s\n",
    "num_bits_per_symbol = 4 # 16 QAM\n",
    "n_ue = 4 # 4 UEs\n",
    "NUM_RX_ANT = 16 # 16 BS antennas\n",
    "num_pilot_symbols = 2\n",
    "\n",
    "# The user terminals (UTs) are equipped with a single antenna\n",
    "# with vertial polarization.\n",
    "UT_ANTENNA = Antenna(polarization='single',\n",
    "                     polarization_type='V',\n",
    "                     antenna_pattern='omni', # Omnidirectional antenna pattern\n",
    "                     carrier_frequency=CARRIER_FREQUENCY)\n",
    "\n",
    "# The base station is equipped with an antenna\n",
    "# array of 8 cross-polarized antennas,\n",
    "# resulting in a total of 16 antenna elements.\n",
    "BS_ARRAY = PanelArray(num_rows_per_panel=2,\n",
    "                      num_cols_per_panel=4,\n",
    "                      polarization='dual',\n",
    "                      polarization_type='cross',\n",
    "                      antenna_pattern='38.901', # 3GPP 38.901 antenna pattern\n",
    "                      carrier_frequency=CARRIER_FREQUENCY)\n",
    "\n",
    "# 3GPP UMa channel model is considered\n",
    "channel_model_uma = UMa(carrier_frequency=CARRIER_FREQUENCY,\n",
    "                    o2i_model='low',\n",
    "                    ut_array=UT_ANTENNA,\n",
    "                    bs_array=BS_ARRAY,\n",
    "                    direction='uplink',\n",
    "                    enable_shadow_fading=False,\n",
    "                    enable_pathloss=False)\n",
    "\n",
    "channel_model_rayleigh = RayleighBlockFading(num_rx=1, num_rx_ant=NUM_RX_ANT, num_tx=n_ue, num_tx_ant=1)\n",
    "\n",
    "constellation = Constellation(\"qam\", num_bits_per_symbol=num_bits_per_symbol)\n",
    "\n",
    "rx_tx_association = np.ones([1, n_ue])\n",
    "sm = StreamManagement(rx_tx_association, 1)\n",
    "\n",
    "# Parameterize the OFDM channel\n",
    "rg = ResourceGrid(num_ofdm_symbols=NUM_OFDM_SYMBOLS, pilot_ofdm_symbol_indices = [2, 11],\n",
    "                  fft_size=FFT_SIZE, num_tx=n_ue,\n",
    "                  pilot_pattern = \"kronecker\",\n",
    "                  subcarrier_spacing=SUBCARRIER_SPACING)\n",
    "\n",
    "rg.show()\n",
    "plt.show()\n",
    "\n",
    "# Parameterize the LDPC code\n",
    "R = 0.5  # rate 1/2\n",
    "N = int(FFT_SIZE * (NUM_OFDM_SYMBOLS - 2) * num_bits_per_symbol)\n",
    "# N = int((FFT_SIZE) * (NUM_OFDM_SYMBOLS - 2) * num_bits_per_symbol)\n",
    "# code length; - 12 because of 11 guard carriers and 1 DC carrier, - 2 becaues of 2 pilot symbols\n",
    "K = int(N * R)  # number of information bits per codeword\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Declaration of the non-IDD and IDD simulation models  <a class=\"anchor\" id=\"non-idd-and-idd-models\"></a>\n",
    "\n",
    "Now, we define the benchmark models. Let us start with the non-IDD models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NonIddModel(Model):\n",
    "    def __init__(self, num_bp_iter=12, detector='lmmse', cest_type=\"LS\", interp=\"lin\", perfect_csi_rayleigh=False):\n",
    "        super().__init__()\n",
    "        self._num_bp_iter = int(num_bp_iter)\n",
    "        ######################################\n",
    "        ## Transmitter\n",
    "        self._binary_source = BinarySource()\n",
    "        self._encoder = LDPC5GEncoder(K, N, num_bits_per_symbol=num_bits_per_symbol)\n",
    "        self._mapper = Mapper(constellation=constellation)\n",
    "        self._rg_mapper = ResourceGridMapper(rg)\n",
    "\n",
    "        # Channel\n",
    "        if perfect_csi_rayleigh:\n",
    "            self._channel_model = channel_model_rayleigh\n",
    "        else:\n",
    "            self._channel_model = channel_model_uma\n",
    "\n",
    "        self._channel = OFDMChannel(channel_model=self._channel_model,\n",
    "                                    resource_grid=rg,\n",
    "                                    add_awgn=True, normalize_channel=True, return_channel=True)\n",
    "        \n",
    "        # Receiver\n",
    "        self._cest_type = cest_type\n",
    "        self._interp = interp\n",
    "\n",
    "        # Channel estimation\n",
    "        self._perfect_csi_rayleigh = perfect_csi_rayleigh\n",
    "        if self._perfect_csi_rayleigh:\n",
    "            self._removeNulledSc = RemoveNulledSubcarriers(rg)\n",
    "        elif cest_type == \"LS\":\n",
    "            self._ls_est = LSChannelEstimator(rg, interpolation_type=interp)\n",
    "        else:\n",
    "            raise NotImplementedError('Not implemented:' + cest_type)\n",
    "\n",
    "        # Detection\n",
    "        if detector == \"lmmse\":\n",
    "            self._detector = LinearDetector(\"lmmse\", 'bit', \"maxlog\", rg, sm, constellation_type=\"qam\",\n",
    "                                            num_bits_per_symbol=num_bits_per_symbol, hard_out=False)\n",
    "        elif detector == \"k-best\":\n",
    "            k = 64\n",
    "            self._detector = KBestDetector('bit', n_ue, k, rg, sm, constellation_type=\"qam\",\n",
    "                                           num_bits_per_symbol=num_bits_per_symbol, hard_out=False)\n",
    "        elif detector == \"ep\":\n",
    "            l = 10\n",
    "            self._detector = EPDetector('bit', rg, sm, num_bits_per_symbol, l=l, hard_out=False)\n",
    "\n",
    "        # Forward error correction (decoder)\n",
    "        self._decoder = LDPC5GDecoder(self._encoder, return_infobits=True, hard_out=True, num_iter=num_bp_iter, cn_type='minsum')\n",
    "\n",
    "    def new_topology(self, batch_size):\n",
    "        \"\"\"Set new topology\"\"\"\n",
    "        if isinstance(self._channel_model, UMa):\n",
    "            # sensible values according to 3GPP standard, no mobility by default\n",
    "            topology = gen_single_sector_topology(batch_size,\n",
    "                                                  n_ue, max_ut_velocity=SPEED,\n",
    "                                                  scenario=\"uma\")\n",
    "            self._channel_model.set_topology(*topology)\n",
    "\n",
    "    @tf.function  # We don't use jit_compile=True to ensure better numerical stability\n",
    "    def call(self, batch_size, ebno_db):\n",
    "        self.new_topology(batch_size)\n",
    "\n",
    "        if len(ebno_db.shape) == 0:\n",
    "            ebno_db = tf.fill([batch_size], ebno_db)\n",
    "\n",
    "        ######################################\n",
    "        ## Transmitter\n",
    "        no = ebnodb2no(ebno_db=ebno_db, num_bits_per_symbol=num_bits_per_symbol,\n",
    "                       coderate=R)  # normalize in OFDM freq. domain\n",
    "        b = self._binary_source([batch_size, n_ue, 1, K])\n",
    "        c = self._encoder(b)\n",
    "        # Modulation\n",
    "        x = self._mapper(c)\n",
    "        x_rg = self._rg_mapper(x)\n",
    "\n",
    "        ######################################\n",
    "        ## Channel\n",
    "        # A batch of new channel realizations is sampled and applied at every inference\n",
    "        no_ = expand_to_rank(no, tf.rank(x_rg))\n",
    "        y, h = self._channel([x_rg, no_])\n",
    "\n",
    "        ######################################\n",
    "        ## Receiver\n",
    "        if self._perfect_csi_rayleigh:\n",
    "            h_hat = self._removeNulledSc(h)\n",
    "            chan_est_var = tf.zeros(tf.shape(h_hat),\n",
    "                                    dtype=tf.float32)  # No channel estimation error when perfect CSI knowledge is assumed\n",
    "        else:\n",
    "            h_hat, chan_est_var = self._ls_est([y, no])\n",
    "\n",
    "        llr_ch = self._detector((y, h_hat, chan_est_var, no))  # detector\n",
    "        b_hat = self._decoder((llr_ch))\n",
    "        return b, b_hat "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we implement the IDD model as follows. We implement IDD with non-resetting LDPC decoder, as in [3], i.e., we forward the LLRs and decoder state from one IDD iteration to the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class IddModel(NonIddModel):  # inherited from NonIddModel\n",
    "    def __init__(self, num_idd_iter=3, num_bp_iter_per_idd_iter=12, cest_type=\"LS\", interp=\"lin\", perfect_csi_rayleigh=False):\n",
    "        super().__init__(num_bp_iter=num_bp_iter_per_idd_iter, detector=\"lmmse\", cest_type=cest_type,\n",
    "                         interp=interp, perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "        # first IDD detector is LMMSE as MMSE-PIC with zero-prior bils down to soft-output LMMSE\n",
    "        self._num_idd_iter = num_idd_iter\n",
    "        self._siso_detector = MMSEPICDetector(output=\"bit\", resource_grid=rg, stream_management=sm,\n",
    "                                              demapping_method='maxlog', constellation=constellation, num_iter=1,\n",
    "                                              hard_out=False)\n",
    "        self._siso_decoder = LDPC5GDecoder(self._encoder, return_infobits=False,\n",
    "                                           num_iter=num_bp_iter_per_idd_iter, stateful=True, hard_out=False, cn_type='minsum')\n",
    "        self._decoder = LDPC5GDecoder(self._encoder, return_infobits=True, stateful=True, hard_out=True, num_iter=num_bp_iter_per_idd_iter, cn_type='minsum')\n",
    "        # last decoder must also be statefull\n",
    "\n",
    "    @tf.function  # We don't use jit_compile=True to ensure better numerical stability\n",
    "    def call(self, batch_size, ebno_db):\n",
    "        self.new_topology(batch_size)\n",
    "\n",
    "        if len(ebno_db.shape) == 0:\n",
    "            ebno_db = tf.fill([batch_size], ebno_db)\n",
    "\n",
    "        ######################################\n",
    "        ## Transmitter\n",
    "        no = ebnodb2no(ebno_db=ebno_db, num_bits_per_symbol=num_bits_per_symbol,\n",
    "                       coderate=R)  # normalize in OFDM freq. domain\n",
    "        b = self._binary_source([batch_size, n_ue, 1, K])\n",
    "        c = self._encoder(b)\n",
    "        # Modulation\n",
    "        x = self._mapper(c)\n",
    "        x_rg = self._rg_mapper(x)\n",
    "\n",
    "        ######################################\n",
    "        ## Channel\n",
    "        # A batch of new channel realizations is sampled and applied at every inference\n",
    "        no_ = expand_to_rank(no, tf.rank(x_rg))\n",
    "        y, h = self._channel([x_rg, no_])\n",
    "\n",
    "        ######################################\n",
    "        ## Receiver\n",
    "        if self._perfect_csi_rayleigh:\n",
    "            h_hat = self._removeNulledSc(h)\n",
    "            chan_est_var = tf.zeros(tf.shape(h_hat),\n",
    "                                    dtype=tf.float32)  # No channel estimation error when perfect CSI knowledge is assumed\n",
    "        else:\n",
    "            h_hat, chan_est_var = self._ls_est([y, no])\n",
    "\n",
    "        llr_ch = self._detector((y, h_hat, chan_est_var, no))  # soft-output LMMSE detection\n",
    "        msg_vn = None\n",
    "\n",
    "        if self._num_idd_iter >= 2:\n",
    "            # perform first iteration outside the while_loop to initialize msg_vn\n",
    "            [llr_dec, msg_vn] = self._siso_decoder((llr_ch, msg_vn))\n",
    "            # forward intrinsic information from decoder\n",
    "\n",
    "            llr_ch = self._siso_detector((y, h_hat, llr_dec, chan_est_var, no))\n",
    "            # forward extrinsic information\n",
    "\n",
    "            def idd_iter(llr_ch, msg_vn, it):\n",
    "                [llr_dec, msg_vn] = self._siso_decoder([llr_ch, msg_vn])\n",
    "                # forward intrinsic information from decoder\n",
    "                llr_ch = self._siso_detector((y, h_hat, llr_dec, chan_est_var, no))\n",
    "                # forward extrinsic information from detector\n",
    "\n",
    "                it += 1\n",
    "                return llr_ch, msg_vn, it\n",
    "\n",
    "            def idd_stop(llr_ch, msg_vn, it):\n",
    "                return tf.less(it, self._num_idd_iter - 1)\n",
    "\n",
    "            it = tf.constant(1)     # we already performed initial detection and one full iteration\n",
    "            llr_ch, msg_vn, it = tf.while_loop(idd_stop, idd_iter, (llr_ch, msg_vn, it), parallel_iterations=1,\n",
    "                                               maximum_iterations=self._num_idd_iter - 1)\n",
    "        else:\n",
    "            # non-idd\n",
    "            pass\n",
    "\n",
    "        [b_hat, _] = self._decoder((llr_ch, msg_vn))    # final hard-output decoding (only returning information bits)\n",
    "        return b, b_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Non-IDD vs. IDD benchmarks <a class=\"anchor\" id=\"non-idd-vs-idd-benchmarks\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-17 17:01:27.452430: I tensorflow/core/util/cuda_solvers.cc:179] Creating GpuSolver handles for stream 0x72e2470\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.1199e-01 | 1.0000e+00 |      250072 |     1179648 |         1024 |        1024 |         7.3 |reached target block errors\n",
      "     -9.0 | 1.8546e-01 | 1.0000e+00 |      218783 |     1179648 |         1024 |        1024 |         0.4 |reached target block errors\n",
      "     -8.0 | 1.0691e-01 | 9.6777e-01 |      126114 |     1179648 |          991 |        1024 |         0.4 |reached target block errors\n",
      "     -7.0 | 2.0105e-02 | 3.8976e-01 |       53363 |     2654208 |          898 |        2304 |         0.9 |reached target block errors\n",
      "     -6.0 | 1.1328e-03 | 3.3758e-02 |       31738 |    28016640 |          821 |       24320 |         8.9 |reached target block errors\n",
      "     -5.0 | 4.1114e-05 | 1.7700e-03 |        1552 |    37748736 |           58 |       32768 |        11.8 |reached max iter       \n",
      "     -4.0 | 1.6159e-06 | 9.1553e-05 |          61 |    37748736 |            3 |       32768 |        11.9 |reached max iter       \n",
      "     -3.0 | 0.0000e+00 | 0.0000e+00 |           0 |    37748736 |            0 |       32768 |        11.8 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = -3.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.1084e-01 | 1.0000e+00 |      248713 |     1179648 |         1024 |        1024 |         4.5 |reached target block errors\n",
      "     -9.0 | 1.8359e-01 | 1.0000e+00 |      216573 |     1179648 |         1024 |        1024 |         0.4 |reached target block errors\n",
      "     -8.0 | 1.0265e-01 | 9.6875e-01 |      121094 |     1179648 |          992 |        1024 |         0.4 |reached target block errors\n",
      "     -7.0 | 1.2308e-02 | 2.8906e-01 |       43556 |     3538944 |          888 |        3072 |         1.3 |reached target block errors\n",
      "     -6.0 | 4.5813e-04 | 1.5503e-02 |       17294 |    37748736 |          508 |       32768 |        13.3 |reached max iter       \n",
      "     -5.0 | 5.4836e-06 | 2.4414e-04 |         207 |    37748736 |            8 |       32768 |        13.3 |reached max iter       \n",
      "     -4.0 | 0.0000e+00 | 0.0000e+00 |           0 |    37748736 |            0 |       32768 |        13.3 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = -4.0 dB.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-17 17:03:06.992986: I tensorflow/compiler/xla/service/service.cc:171] XLA service 0x7f95a802f340 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2023-02-17 17:03:06.993030: I tensorflow/compiler/xla/service/service.cc:179]   StreamExecutor device (0): A100-SXM4-80GB, Compute Capability 8.0\n",
      "2023-02-17 17:03:07.049757: I tensorflow/compiler/jit/xla_compilation_cache.cc:399] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.1104e-01 | 1.0000e+00 |      248952 |     1179648 |         1024 |        1024 |         7.3 |reached target block errors\n",
      "     -9.0 | 1.8529e-01 | 1.0000e+00 |      218572 |     1179648 |         1024 |        1024 |         2.4 |reached target block errors\n",
      "     -8.0 | 1.1593e-01 | 9.9805e-01 |      136757 |     1179648 |         1022 |        1024 |         2.4 |reached target block errors\n",
      "     -7.0 | 2.1963e-02 | 5.8398e-01 |       38863 |     1769472 |          897 |        1536 |         3.7 |reached target block errors\n",
      "     -6.0 | 9.7703e-04 | 4.9639e-02 |       18729 |    19169280 |          826 |       16640 |        39.7 |reached target block errors\n",
      "     -5.0 | 3.2451e-05 | 1.1292e-03 |        1225 |    37748736 |           37 |       32768 |        78.1 |reached max iter       \n",
      "     -4.0 | 0.0000e+00 | 0.0000e+00 |           0 |    37748736 |            0 |       32768 |        78.8 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = -4.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.1228e-01 | 1.0000e+00 |      250417 |     1179648 |         1024 |        1024 |         9.8 |reached target block errors\n",
      "     -9.0 | 1.8675e-01 | 9.9902e-01 |      220302 |     1179648 |         1023 |        1024 |         0.6 |reached target block errors\n",
      "     -8.0 | 9.2469e-02 | 7.1719e-01 |      136351 |     1474560 |          918 |        1280 |         0.8 |reached target block errors\n",
      "     -7.0 | 3.4421e-03 | 5.0049e-02 |       64967 |    18874368 |          820 |       16384 |        10.2 |reached target block errors\n",
      "     -6.0 | 7.5950e-05 | 1.0986e-03 |        2867 |    37748736 |           36 |       32768 |        21.1 |reached max iter       \n",
      "     -5.0 | 0.0000e+00 | 0.0000e+00 |           0 |    37748736 |            0 |       32768 |        21.0 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = -5.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.1294e-01 | 1.0000e+00 |      251195 |     1179648 |         1024 |        1024 |         9.2 |reached target block errors\n",
      "     -9.0 | 1.8671e-01 | 9.9902e-01 |      220253 |     1179648 |         1023 |        1024 |         0.9 |reached target block errors\n",
      "     -8.0 | 7.4010e-02 | 5.7487e-01 |      130958 |     1769472 |          883 |        1536 |         1.3 |reached target block errors\n",
      "     -7.0 | 1.5175e-03 | 1.5991e-02 |       57284 |    37748736 |          524 |       32768 |        29.2 |reached max iter       \n",
      "     -6.0 | 3.4067e-05 | 3.9673e-04 |        1286 |    37748736 |           13 |       32768 |        29.2 |reached max iter       \n",
      "     -5.0 | 0.0000e+00 | 0.0000e+00 |           0 |    37748736 |            0 |       32768 |        29.1 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = -5.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 2.9949e-01 | 1.0000e+00 |      353290 |     1179648 |         1024 |        1024 |        13.3 |reached target block errors\n",
      "     -8.0 | 2.6353e-01 | 1.0000e+00 |      310867 |     1179648 |         1024 |        1024 |         0.5 |reached target block errors\n",
      "     -6.0 | 2.2797e-01 | 1.0000e+00 |      268929 |     1179648 |         1024 |        1024 |         0.5 |reached target block errors\n",
      "     -4.0 | 1.5027e-01 | 9.1504e-01 |      177270 |     1179648 |          937 |        1024 |         0.5 |reached target block errors\n",
      "     -2.0 | 4.6400e-02 | 3.7934e-01 |      123156 |     2654208 |          874 |        2304 |         1.1 |reached target block errors\n",
      "      0.0 | 9.6942e-03 | 8.5012e-02 |      108640 |    11206656 |          827 |        9728 |         4.4 |reached target block errors\n",
      "      2.0 | 2.8174e-03 | 2.3895e-02 |      106355 |    37748736 |          783 |       32768 |        14.4 |reached max iter       \n",
      "      4.0 | 1.2162e-03 | 9.1248e-03 |       45909 |    37748736 |          299 |       32768 |        14.2 |reached max iter       \n",
      "      6.0 | 8.9455e-04 | 5.6763e-03 |       33768 |    37748736 |          186 |       32768 |        14.4 |reached max iter       \n",
      "      8.0 | 9.0543e-04 | 5.9204e-03 |       34179 |    37748736 |          194 |       32768 |        14.5 |reached max iter       \n",
      "     10.0 | 8.2948e-04 | 5.3711e-03 |       31312 |    37748736 |          176 |       32768 |        14.4 |reached max iter       \n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 3.0007e-01 | 1.0000e+00 |      353972 |     1179648 |         1024 |        1024 |         8.0 |reached target block errors\n",
      "     -8.0 | 2.6328e-01 | 1.0000e+00 |      310580 |     1179648 |         1024 |        1024 |         0.6 |reached target block errors\n",
      "     -6.0 | 2.2631e-01 | 1.0000e+00 |      266961 |     1179648 |         1024 |        1024 |         0.5 |reached target block errors\n",
      "     -4.0 | 1.3657e-01 | 8.9941e-01 |      161104 |     1179648 |          921 |        1024 |         0.5 |reached target block errors\n",
      "     -2.0 | 3.9914e-02 | 3.2969e-01 |      117711 |     2949120 |          844 |        2560 |         1.3 |reached target block errors\n",
      "      0.0 | 7.0897e-03 | 6.6976e-02 |      100360 |    14155776 |          823 |       12288 |         6.0 |reached target block errors\n",
      "      2.0 | 1.9734e-03 | 1.5930e-02 |       74492 |    37748736 |          522 |       32768 |        15.8 |reached max iter       \n",
      "      4.0 | 1.1982e-03 | 7.9956e-03 |       45231 |    37748736 |          262 |       32768 |        15.6 |reached max iter       \n",
      "      6.0 | 7.5589e-04 | 5.1880e-03 |       28534 |    37748736 |          170 |       32768 |        15.7 |reached max iter       \n",
      "      8.0 | 7.9359e-04 | 5.7983e-03 |       29957 |    37748736 |          190 |       32768 |        15.9 |reached max iter       \n",
      "     10.0 | 9.1884e-04 | 6.5918e-03 |       34685 |    37748736 |          216 |       32768 |        15.6 |reached max iter       \n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 3.0617e-01 | 1.0000e+00 |      361176 |     1179648 |         1024 |        1024 |        10.3 |reached target block errors\n",
      "     -8.0 | 2.6871e-01 | 1.0000e+00 |      316981 |     1179648 |         1024 |        1024 |         2.5 |reached target block errors\n",
      "     -6.0 | 2.3244e-01 | 1.0000e+00 |      274192 |     1179648 |         1024 |        1024 |         2.5 |reached target block errors\n",
      "     -4.0 | 1.5877e-01 | 9.7461e-01 |      187296 |     1179648 |          998 |        1024 |         2.5 |reached target block errors\n",
      "     -2.0 | 4.1144e-02 | 3.8325e-01 |      109206 |     2654208 |          883 |        2304 |         5.7 |reached target block errors\n",
      "      0.0 | 5.5377e-03 | 5.5361e-02 |       94721 |    17104896 |          822 |       14848 |        36.8 |reached target block errors\n",
      "      2.0 | 1.6635e-03 | 1.2238e-02 |       62795 |    37748736 |          401 |       32768 |        81.3 |reached max iter       \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      4.0 | 1.1597e-03 | 7.0496e-03 |       43779 |    37748736 |          231 |       32768 |        81.0 |reached max iter       \n",
      "      6.0 | 7.4980e-04 | 4.6082e-03 |       28304 |    37748736 |          151 |       32768 |        81.1 |reached max iter       \n",
      "      8.0 | 7.2082e-04 | 4.0588e-03 |       27210 |    37748736 |          133 |       32768 |        81.0 |reached max iter       \n",
      "     10.0 | 7.5923e-04 | 5.4932e-03 |       28660 |    37748736 |          180 |       32768 |        81.2 |reached max iter       \n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 3.0235e-01 | 1.0000e+00 |      356667 |     1179648 |         1024 |        1024 |        13.4 |reached target block errors\n",
      "     -8.0 | 2.6703e-01 | 1.0000e+00 |      315001 |     1179648 |         1024 |        1024 |         0.7 |reached target block errors\n",
      "     -6.0 | 2.2941e-01 | 1.0000e+00 |      270625 |     1179648 |         1024 |        1024 |         0.7 |reached target block errors\n",
      "     -4.0 | 1.4582e-01 | 8.0547e-01 |      215021 |     1474560 |         1031 |        1280 |         0.9 |reached target block errors\n",
      "     -2.0 | 2.9707e-02 | 2.0083e-01 |      148937 |     5013504 |          874 |        4352 |         3.1 |reached target block errors\n",
      "      0.0 | 4.4446e-03 | 3.1556e-02 |      133699 |    30081024 |          824 |       26112 |        18.2 |reached target block errors\n",
      "      2.0 | 1.3493e-03 | 8.9722e-03 |       50934 |    37748736 |          294 |       32768 |        23.0 |reached max iter       \n",
      "      4.0 | 7.8954e-04 | 4.4250e-03 |       29804 |    37748736 |          145 |       32768 |        22.5 |reached max iter       \n",
      "      6.0 | 7.4360e-04 | 4.2725e-03 |       28070 |    37748736 |          140 |       32768 |        22.4 |reached max iter       \n",
      "      8.0 | 8.4721e-04 | 5.0659e-03 |       31981 |    37748736 |          166 |       32768 |        22.7 |reached max iter       \n",
      "     10.0 | 8.7531e-04 | 5.2490e-03 |       33042 |    37748736 |          172 |       32768 |        22.5 |reached max iter       \n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "    -10.0 | 3.0028e-01 | 1.0000e+00 |      354220 |     1179648 |         1024 |        1024 |        13.9 |reached target block errors\n",
      "     -8.0 | 2.6649e-01 | 1.0000e+00 |      314360 |     1179648 |         1024 |        1024 |         1.0 |reached target block errors\n",
      "     -6.0 | 2.3061e-01 | 1.0000e+00 |      272035 |     1179648 |         1024 |        1024 |         1.0 |reached target block errors\n",
      "     -4.0 | 1.4171e-01 | 7.7031e-01 |      208953 |     1474560 |          986 |        1280 |         1.3 |reached target block errors\n",
      "     -2.0 | 2.3093e-02 | 1.4096e-01 |      156639 |     6782976 |          830 |        5888 |         5.7 |reached target block errors\n",
      "      0.0 | 3.2411e-03 | 1.9897e-02 |      122348 |    37748736 |          652 |       32768 |        31.6 |reached max iter       \n",
      "      2.0 | 1.2333e-03 | 6.7444e-03 |       46556 |    37748736 |          221 |       32768 |        31.2 |reached max iter       \n",
      "      4.0 | 7.1028e-04 | 3.7842e-03 |       26812 |    37748736 |          124 |       32768 |        31.6 |reached max iter       \n",
      "      6.0 | 6.5186e-04 | 3.6011e-03 |       24607 |    37748736 |          118 |       32768 |        31.6 |reached max iter       \n",
      "      8.0 | 8.5492e-04 | 4.6997e-03 |       32272 |    37748736 |          154 |       32768 |        31.5 |reached max iter       \n",
      "     10.0 | 7.2771e-04 | 5.0354e-03 |       27470 |    37748736 |          165 |       32768 |        32.2 |reached max iter       \n"
     ]
    }
   ],
   "source": [
    "# Range of SNR (dB)\n",
    "snr_range_cest = np.linspace(ebno_db_min_cest, ebno_db_max_cest, num_steps)\n",
    "snr_range_perf_csi = np.linspace(ebno_db_min_perf_csi, ebno_db_max_perf_csi, num_steps)\n",
    "\n",
    "def run_idd_sim(snr_range, perfect_csi_rayleigh):\n",
    "    lmmse = NonIddModel(detector=\"lmmse\", perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "    k_best = NonIddModel(detector=\"k-best\", perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "    ep = NonIddModel(detector=\"ep\", perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "    idd2 = IddModel(num_idd_iter=2, perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "    idd3 = IddModel(num_idd_iter=3, perfect_csi_rayleigh=perfect_csi_rayleigh)\n",
    "\n",
    "    ber_lmmse, bler_lmmse = sim_ber(lmmse,\n",
    "                                    snr_range,\n",
    "                                    batch_size=batch_size,\n",
    "                                    max_mc_iter=num_iter,\n",
    "                                    num_target_block_errors=int(batch_size * num_iter * 0.1))\n",
    "\n",
    "    ber_ep, bler_ep = sim_ber(ep,\n",
    "                              snr_range,\n",
    "                              batch_size=batch_size,\n",
    "                              max_mc_iter=num_iter,\n",
    "                              num_target_block_errors=int(batch_size * num_iter * 0.1))\n",
    "\n",
    "    ber_kbest, bler_kbest = sim_ber(k_best,\n",
    "                                    snr_range,\n",
    "                                    batch_size=batch_size,\n",
    "                                    max_mc_iter=num_iter,\n",
    "                                    num_target_block_errors=int(batch_size * num_iter * 0.1))\n",
    "\n",
    "    ber_idd2, bler_idd2 = sim_ber(idd2,\n",
    "                                  snr_range,\n",
    "                                  batch_size=batch_size,\n",
    "                                  max_mc_iter=num_iter,\n",
    "                                  num_target_block_errors=int(batch_size * num_iter * 0.1))\n",
    "\n",
    "    ber_idd3, bler_idd3 = sim_ber(idd3,\n",
    "                                  snr_range,\n",
    "                                  batch_size=batch_size,\n",
    "                                  max_mc_iter=num_iter,\n",
    "                                  num_target_block_errors=int(batch_size * num_iter * 0.1))\n",
    "\n",
    "    return bler_lmmse, bler_ep, bler_kbest, bler_idd2, bler_idd3\n",
    "\n",
    "\n",
    "BLER = {}\n",
    "\n",
    "# Perfect CSI\n",
    "bler_lmmse, bler_ep, bler_kbest, bler_idd2, bler_idd3 = run_idd_sim(snr_range_perf_csi, perfect_csi_rayleigh=True)\n",
    "BLER['Perf. CSI / LMMSE'] = bler_lmmse\n",
    "BLER['Perf. CSI / EP'] = bler_ep\n",
    "BLER['Perf. CSI / K-Best'] = bler_kbest\n",
    "BLER['Perf. CSI / IDD2'] = bler_idd2\n",
    "BLER['Perf. CSI / IDD3'] = bler_idd3\n",
    "\n",
    "# Estimated CSI\n",
    "bler_lmmse, bler_ep, bler_kbest, bler_idd2, bler_idd3 = run_idd_sim(snr_range_cest, perfect_csi_rayleigh=False)\n",
    "BLER['Ch. Est. / LMMSE'] = bler_lmmse\n",
    "BLER['Ch. Est. / EP'] = bler_ep\n",
    "BLER['Ch. Est. / K-Best'] = bler_kbest\n",
    "BLER['Ch. Est. / IDD2'] = bler_idd2\n",
    "BLER['Ch. Est. / IDD3'] = bler_idd3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot the simulation results and observe that IDD outperforms the non-iterative methods by about 1 dB in the scenario with iid Rayleigh fading channels and perfect CSI. In the scenario with 3GPP UMa channels and estimated CSI, IDD performs slightly better than K-best, at considerably lower runtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1600x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(16,7))\n",
    "fig.suptitle(f\"{n_ue}x{NUM_RX_ANT} MU-MIMO UL | {2**num_bits_per_symbol}-QAM\")\n",
    "\n",
    "## Perfect CSI Rayleigh\n",
    "ax[0].set_title(\"Perfect CSI iid. Rayleigh\")\n",
    "ax[0].semilogy(snr_range_perf_csi, BLER['Perf. CSI / LMMSE'], 'x-', label='LMMSE', c='C0')\n",
    "ax[0].semilogy(snr_range_perf_csi, BLER['Perf. CSI / EP'], 'o--', label='EP', c='C0')\n",
    "ax[0].semilogy(snr_range_perf_csi, BLER['Perf. CSI / K-Best'], 's-.', label='K-Best', c='C0')\n",
    "ax[0].semilogy(snr_range_perf_csi, BLER['Perf. CSI / IDD2'], 'd:', label=r'IDD $I=2$', c='C1')\n",
    "ax[0].semilogy(snr_range_perf_csi, BLER['Perf. CSI / IDD3'], 'd:', label=r'IDD $I=3$', c='C2')\n",
    "\n",
    "ax[0].set_xlabel(r\"$E_b/N0$\")\n",
    "ax[0].set_ylabel(\"BLER\")\n",
    "ax[0].set_ylim((1e-4, 1.0))\n",
    "ax[0].legend()\n",
    "ax[0].grid(True)\n",
    "\n",
    "## Estimated CSI Rayleigh\n",
    "ax[1].set_title(\"Estimated CSI 3GPP UMa\")\n",
    "ax[1].semilogy(snr_range_cest, BLER['Ch. Est. / LMMSE'], 'x-', label='LMMSE', c='C0')\n",
    "ax[1].semilogy(snr_range_cest, BLER['Ch. Est. / EP'], 'o--', label='EP', c='C0')\n",
    "ax[1].semilogy(snr_range_cest, BLER['Ch. Est. / K-Best'], 's-.', label='K-Best', c='C0')\n",
    "ax[1].semilogy(snr_range_cest, BLER['Ch. Est. / IDD2'], 'd:', label=r'IDD $I=2$', c='C1')\n",
    "ax[1].semilogy(snr_range_cest, BLER['Ch. Est. / IDD3'], 'd:', label=r'IDD $I=3$', c='C2')\n",
    "\n",
    "ax[1].set_xlabel(r\"$E_b/N0$\")\n",
    "ax[1].set_ylabel(\"BLER\")\n",
    "ax[1].set_ylim((1e-3, 1.0))\n",
    "ax[1].legend()\n",
    "ax[1].grid(True)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion: Optimizing IDD with machine learning <a class=\"anchor\" id=\"optimizing-idd-with-ml\"></a> \n",
    "Recent work [4] showed that IDD can be significantly improved by deep-unfolding, which applies machine learning to automatically tune hyperparameters of classical algorithms. The proposed *Deep-Unfolded Interleaved Detection and Decoding* method showed performance gains of up to 1.4 dB at the same computational complexity. The simulation code is available on [Github](https://github.com/IIP-Group/DUIDD). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List of comments <a class=\"anchor\" id=\"list-of-comments\"></a>\n",
    "\n",
    "- As discussed in [3], IDD receivers with a non-resetting decoder converge faster than with resetting decoders. However, a resetting decoder (which does not forward `msg_vn`) might perform slightly better for a large number of message passing decoding iterations. Among other quantities, a scaling of the forwarded decoder state is optimized in the DUIDD receiver [4].\n",
    "- With estimated channels, we observed that the MMSE-PIC output LLRs become large, much larger as with non-iterative receive processing.\n",
    "\n",
    "### Acknowledgements\n",
    "- We thank Dr. Jakob Hoydis and Dr. Sebastian Cammerer for valuable suggestions and comments on the code and content of this notebook.\n",
    "- We thank Seyed Hadi Mirfarshbafan, Dr. Chris Dick and Prof. Dr. Christoph Studer for contributing valuable feedback to this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References <a class=\"anchor\" id=\"references\"></a>\n",
    "\n",
    "[1] B. Hochwald and S. Ten Brink, *\"Achieving near-capacity on a multiple-antenna channel,\"* IEEE Trans. Commun., vol. 51, no. 3, pp. 389–399, Mar. 2003.\n",
    "\n",
    "[2] C. Studer, S. Fateh, and D. Seethaler, *\"ASIC implementation of soft-input soft-output MIMO detection\n",
    "using MMSE parallel interference cancellation,\"* IEEE Journal of Solid-State Circuits, vol. 46, no. 7, pp. 1754–1765, Jul. 2011.\n",
    "\n",
    "[3] W.-C. Sun, W.-H. Wu, C.-H. Yang, and Y.-L. Ueng, *\"An iterative detection and decoding receiver for LDPC-coded MIMO systems,\"* IEEE Trans. Circuits Syst. I, vol. 62, no. 10, pp. 2512–2522, Oct. 2015.\n",
    "\n",
    "[4] R. Wiesmayr, C. Dick, J. Hoydis, and C. Studer, *\"DUIDD: Deep-unfolded interleaved detection and decoding for MIMO wireless systems,\"* in Asilomar Conf. Signals, Syst., Comput., Oct. 2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}