{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03191cf2",
   "metadata": {},
   "source": [
    "# OFDM-based Communications with Superimposed Pilots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a59c999",
   "metadata": {},
   "source": [
    "This notebook provides an implementation of superimposed pilots for OFDM communication systems, based on the approach presented in \"[End-to-End Learning for OFDM: From Neural Receivers to Pilotless Communication](https://ieeexplore.ieee.org/abstract/document/9508784)\" [1].\n",
    "\n",
    "We focus specifically on neural network-based receivers that leverage superimposed pilot signals."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d6fba6",
   "metadata": {},
   "source": [
    "## Configuration and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d84bd69e-59f2-4a42-8dd3-730c8c1d821e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T22:00:35.544336Z",
     "iopub.status.busy": "2026-08-06T22:00:35.544207Z",
     "iopub.status.idle": "2026-08-06T22:00:38.713408Z",
     "shell.execute_reply": "2026-08-06T22:00:38.712818Z"
    }
   },
   "outputs": [],
   "source": [
    "# Import Sionna\n",
    "try:\n",
    "    import sionna.phy\n",
    "except ImportError as e:\n",
    "    import os\n",
    "    import sys\n",
    "    if 'google.colab' in sys.modules:\n",
    "       # Install Sionna in Google Colab\n",
    "       print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n",
    "       os.system(\"pip install sionna\")\n",
    "       os.kill(os.getpid(), 5)\n",
    "    else:\n",
    "       raise e\n",
    "\n",
    "import math\n",
    "import numpy as np\n",
    "import warnings\n",
    "\n",
    "# For plotting\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# For the implementation of the neural receiver\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from sionna.phy import Block\n",
    "from sionna.phy.channel.tr38901 import Antenna, AntennaArray, CDL\n",
    "from sionna.phy.channel import OFDMChannel\n",
    "from sionna.phy.mimo import StreamManagement\n",
    "from sionna.phy.ofdm import (\n",
    "    ResourceGrid,\n",
    "    ResourceGridMapper,\n",
    "    ResourceGridDemapper,\n",
    "    LSChannelEstimator,\n",
    "    LMMSEEqualizer,\n",
    "    RemoveNulledSubcarriers,\n",
    ")\n",
    "from sionna.phy.utils import ebnodb2no, insert_dims, expand_to_rank, sim_ber, PlotBER\n",
    "from sionna.phy.fec.ldpc import LDPC5GEncoder, LDPC5GDecoder\n",
    "from sionna.phy.mapping import Mapper, Demapper, BinarySource, QAMSource\n",
    "\n",
    "# Set seed for reproducible results\n",
    "sionna.phy.config.seed = 42\n",
    "device = sionna.phy.config.device\n",
    "warnings.filterwarnings(\n",
    "    \"ignore\",\n",
    "    message=\"Torchinductor does not support code generation for complex operators.*\",\n",
    ")\n",
    "warnings.filterwarnings(\n",
    "    \"ignore\",\n",
    "    message=\"`torch._prims_common.check` is deprecated.*\",\n",
    "    category=FutureWarning,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64d17183",
   "metadata": {},
   "source": [
    "## Simulation Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4dea9622",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T22:00:38.716316Z",
     "iopub.status.busy": "2026-08-06T22:00:38.716033Z",
     "iopub.status.idle": "2026-08-06T22:00:39.003341Z",
     "shell.execute_reply": "2026-08-06T22:00:39.002377Z"
    }
   },
   "outputs": [],
   "source": [
    "# Bit per channel use\n",
    "NUM_BITS_PER_SYMBOL = 2 # QPSK\n",
    "\n",
    "# Minimum value of Eb/N0 [dB] for simulations\n",
    "EBN0_DB_MIN = 0.0\n",
    "\n",
    "# Maximum value of Eb/N0 [dB] for simulations\n",
    "EBN0_DB_MAX = 10.0\n",
    "\n",
    "# How many examples are processed by Sionna in parallel\n",
    "BATCH_SIZE = 128\n",
    "\n",
    "# Coding rate\n",
    "CODERATE = 0.5\n",
    "\n",
    "# Number of convolutional channels\n",
    "NUM_CONV_CHANNELS = 128\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",
    "# For example, considering a system with 2 RX and 4 TX, the RX-TX\n",
    "# association matrix could be\n",
    "# [ [1 , 1, 0, 0],\n",
    "#   [0 , 0, 1, 1] ]\n",
    "# which indicates that the RX 0 receives from TX 0 and 1, and RX 1 receives from\n",
    "# TX 2 and 3.\n",
    "#\n",
    "# In this notebook, as we have only a single transmitter and receiver,\n",
    "# the RX-TX association matrix is simply:\n",
    "RX_TX_ASSOCIATION = np.array([[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 easy. However, it can get more involved\n",
    "# for simulations with many transmitters and receivers.\n",
    "STREAM_MANAGEMENT = StreamManagement(RX_TX_ASSOCIATION, 1)\n",
    "\n",
    "# Resource grid configuration without DMRS pilots for superimposed pilot transmission\n",
    "RESOURCE_GRID_0P = ResourceGrid(num_ofdm_symbols=14,\n",
    "                                fft_size=76,\n",
    "                                subcarrier_spacing=30e3,\n",
    "                                num_tx=1,\n",
    "                                num_streams_per_tx=1,\n",
    "                                cyclic_prefix_length=6)\n",
    "\n",
    "# Resource grid configuration with two DMRS pilots for baseline comparison\n",
    "RESOURCE_GRID_2P = ResourceGrid(num_ofdm_symbols=14,\n",
    "                                fft_size=76,\n",
    "                                subcarrier_spacing=30e3,\n",
    "                                num_tx=1,\n",
    "                                num_streams_per_tx=1,\n",
    "                                pilot_pattern=\"kronecker\",\n",
    "                                pilot_ofdm_symbol_indices=[2,11],\n",
    "                                cyclic_prefix_length=6)\n",
    "\n",
    "# Grid dimensions for neural network\n",
    "NUM_OFDM_SYMBOLS = RESOURCE_GRID_0P.num_ofdm_symbols\n",
    "FFT_SIZE = RESOURCE_GRID_0P.fft_size\n",
    "\n",
    "# Carrier frequency in Hz.\n",
    "CARRIER_FREQUENCY = 2.6e9\n",
    "\n",
    "# Antenna setting\n",
    "UT_ARRAY = Antenna(polarization=\"single\",\n",
    "                   polarization_type=\"V\",\n",
    "                   antenna_pattern=\"omni\",\n",
    "                   carrier_frequency=CARRIER_FREQUENCY)\n",
    "BS_ARRAY = AntennaArray(num_rows=1,\n",
    "                        num_cols=1,\n",
    "                        polarization=\"single\",\n",
    "                        polarization_type=\"V\",\n",
    "                        antenna_pattern=\"omni\",\n",
    "                        carrier_frequency=CARRIER_FREQUENCY)\n",
    "\n",
    "# Nominal delay spread in [s]. Please see the CDL documentation\n",
    "# about how to choose this value.\n",
    "DELAY_SPREAD = 100e-9\n",
    "\n",
    "# The `direction` determines if the UT or BS is transmitting.\n",
    "# In the `uplink`, the UT is transmitting.\n",
    "DIRECTION = \"uplink\"\n",
    "\n",
    "# Suitable values are [\"A\", \"B\", \"C\", \"D\", \"E\"]\n",
    "CDL_MODEL = \"C\"\n",
    "\n",
    "# UT speed [m/s]. BSs are always assumed to be fixed.\n",
    "# The direction of travel will chosen randomly within the x-y plane.\n",
    "SPEED = 3.0\n",
    "\n",
    "# Configure a channel impulse response (CIR) generator for the CDL model.\n",
    "CDL_CHANNEL = CDL(CDL_MODEL,\n",
    "                  DELAY_SPREAD,\n",
    "                  CARRIER_FREQUENCY,\n",
    "                  UT_ARRAY,\n",
    "                  BS_ARRAY,\n",
    "                  DIRECTION,\n",
    "                  min_speed=SPEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75eb3265",
   "metadata": {},
   "source": [
    "Let's inspect the two OFDM resource grids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b3b1bbd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T22:00:39.005445Z",
     "iopub.status.busy": "2026-08-06T22:00:39.005302Z",
     "iopub.status.idle": "2026-08-06T22:00:39.386893Z",
     "shell.execute_reply": "2026-08-06T22:00:39.386086Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Resource grid without DMRS pilots\n",
    "plot = RESOURCE_GRID_0P.show();\n",
    "plt.title(\"OFDM Resource grid without DMRS pilots\");\n",
    "\n",
    "# Resource grid with DMRS pilots\n",
    "plot = RESOURCE_GRID_2P.show();\n",
    "plt.title(\"OFDM Resource grid with DMRS pilots\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8eb9259",
   "metadata": {},
   "source": [
    "## Neural Receiver"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50c85b8f",
   "metadata": {},
   "source": [
    "The next cell defines the PyTorch modules that implement the neural receiver.\n",
    "Convolutional layers are leveraged to efficiently process the 2D resource grid that is fed as input to the neural receiver and residual (skip) connections are used to prevent gradient vanishing during training [2].\n",
    "\n",
    "For convenience, a PyTorch module that implements a *residual block* is first defined. The neural receiver is then built by stacking multiple such blocks. The following figure shows the architecture of the neural receiver."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f2c755",
   "metadata": {},
   "source": [
    "![Neural 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf95e03d-882b-4af7-aafa-4b86a3ab334a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T22:00:39.389277Z",
     "iopub.status.busy": "2026-08-06T22:00:39.389140Z",
     "iopub.status.idle": "2026-08-06T22:00:39.396888Z",
     "shell.execute_reply": "2026-08-06T22:00:39.396197Z"
    }
   },
   "outputs": [],
   "source": [
    "class ResidualBlock(nn.Module):\n",
    "    \"\"\"\n",
    "    Convolutional residual block with two convolutional layers, ReLU activation,\n",
    "    layer normalization, and a skip connection.\n",
    "\n",
    "    The number of convolutional channels of the input must match num_conv_channels\n",
    "    for the skip connection to work.\n",
    "\n",
    "    Input shape: [batch_size, num_conv_channels, num_ofdm_symbols, num_subcarriers]\n",
    "    Output shape: [batch_size, num_conv_channels, num_ofdm_symbols, num_subcarriers]\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, num_conv_channels: int, num_ofdm_symbols: int, fft_size: int):\n",
    "        super().__init__()\n",
    "        # Layer normalization over the last three dimensions (C, H, W)\n",
    "        self._layer_norm_1 = nn.LayerNorm([num_conv_channels, num_ofdm_symbols, fft_size])\n",
    "        self._conv_1 = nn.Conv2d(\n",
    "            in_channels=num_conv_channels,\n",
    "            out_channels=num_conv_channels,\n",
    "            kernel_size=3,\n",
    "            padding=1,  # 'same' padding\n",
    "        )\n",
    "        self._layer_norm_2 = nn.LayerNorm([num_conv_channels, num_ofdm_symbols, fft_size])\n",
    "        self._conv_2 = nn.Conv2d(\n",
    "            in_channels=num_conv_channels,\n",
    "            out_channels=num_conv_channels,\n",
    "            kernel_size=3,\n",
    "            padding=1,\n",
    "        )\n",
    "\n",
    "    def forward(self, inputs: torch.Tensor) -> torch.Tensor:\n",
    "        z = self._layer_norm_1(inputs)\n",
    "        z = F.relu(z)\n",
    "        z = self._conv_1(z)\n",
    "        z = self._layer_norm_2(z)\n",
    "        z = F.relu(z)\n",
    "        z = self._conv_2(z)\n",
    "        # Skip connection\n",
    "        z = z + inputs\n",
    "        return z\n",
    "\n",
    "\n",
    "class NeuralReceiver(nn.Module):\n",
    "    \"\"\"\n",
    "    Residual convolutional neural receiver for superimposed pilots.\n",
    "\n",
    "    This neural receiver is fed with the post-DFT received samples, forming a\n",
    "    resource grid of size num_ofdm_symbols x fft_size, and computes LLRs on\n",
    "    the transmitted coded bits.\n",
    "\n",
    "    Input\n",
    "    -----\n",
    "    y : [batch_size, num_rx, num_rx_antenna, num_ofdm_symbols, num_subcarriers], complex\n",
    "        Received post-DFT samples.\n",
    "    no : [batch_size], float\n",
    "        Noise variance.\n",
    "\n",
    "    Output\n",
    "    ------\n",
    "    llr : [batch_size, 1, 1, num_ofdm_symbols, num_subcarriers, num_bits_per_symbol], float\n",
    "        LLRs on the transmitted bits.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        num_conv_channels: int,\n",
    "        num_bits_per_symbol: int,\n",
    "        num_ofdm_symbols: int,\n",
    "        fft_size: int,\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self._num_bits_per_symbol = num_bits_per_symbol\n",
    "        self._num_ofdm_symbols = num_ofdm_symbols\n",
    "        self._fft_size = fft_size\n",
    "\n",
    "        # Input convolution: 2*num_rx_antenna + 1 input channels (real, imag, noise)\n",
    "        # For single antenna: 2*1 + 1 = 3 input channels\n",
    "        num_input_channels = 2 * 1 + 1\n",
    "        self._input_conv = nn.Conv2d(\n",
    "            in_channels=num_input_channels,\n",
    "            out_channels=num_conv_channels,\n",
    "            kernel_size=3,\n",
    "            padding=1,\n",
    "        )\n",
    "        # Residual blocks\n",
    "        self._res_block_1 = ResidualBlock(num_conv_channels, num_ofdm_symbols, fft_size)\n",
    "        self._res_block_2 = ResidualBlock(num_conv_channels, num_ofdm_symbols, fft_size)\n",
    "        self._res_block_3 = ResidualBlock(num_conv_channels, num_ofdm_symbols, fft_size)\n",
    "        self._res_block_4 = ResidualBlock(num_conv_channels, num_ofdm_symbols, fft_size)\n",
    "        # Output convolution\n",
    "        self._output_conv = nn.Conv2d(\n",
    "            in_channels=num_conv_channels,\n",
    "            out_channels=num_bits_per_symbol,\n",
    "            kernel_size=3,\n",
    "            padding=1,\n",
    "        )\n",
    "\n",
    "    def forward(self, y: torch.Tensor, no: torch.Tensor) -> torch.Tensor:\n",
    "        # y: [batch, num_rx, num_rx_ant, num_ofdm_symbols, num_subcarriers]\n",
    "        # no: [batch]\n",
    "\n",
    "        # Assuming a single receiver, remove the num_rx dimension\n",
    "        y = y.squeeze(1)  # [batch, num_rx_ant, num_ofdm_symbols, num_subcarriers]\n",
    "\n",
    "        # Feeding the noise power in log10 scale helps with the performance\n",
    "        no_log = torch.log10(no)\n",
    "\n",
    "        # Stack real and imaginary components\n",
    "        y_real = y.real  # [batch, num_rx_ant, time, freq]\n",
    "        y_imag = y.imag  # [batch, num_rx_ant, time, freq]\n",
    "\n",
    "        # Reshape noise to [batch, 1, 1, 1] and broadcast\n",
    "        batch_size = y.shape[0]\n",
    "        no_expanded = no_log.view(-1, 1, 1, 1)\n",
    "        no_expanded = no_expanded.expand(batch_size, 1, y.shape[2], y.shape[3])\n",
    "\n",
    "        # Concatenate: [batch, 2*num_rx_ant + 1, time, freq]\n",
    "        z = torch.cat([y_real, y_imag, no_expanded], dim=1)\n",
    "\n",
    "        # Input conv\n",
    "        z = self._input_conv(z)\n",
    "        # Residual blocks\n",
    "        z = self._res_block_1(z)\n",
    "        z = self._res_block_2(z)\n",
    "        z = self._res_block_3(z)\n",
    "        z = self._res_block_4(z)\n",
    "        # Output conv: [batch, num_bits_per_symbol, time, freq]\n",
    "        z = self._output_conv(z)\n",
    "\n",
    "        # Transpose to [batch, time, freq, num_bits_per_symbol]\n",
    "        z = z.permute(0, 2, 3, 1)\n",
    "\n",
    "        # Reshape the input to fit what the resource grid demapper expects\n",
    "        # Add dimensions for num_rx and num_rx_ant: [batch, 1, 1, time, freq, num_bits_per_symbol]\n",
    "        z = insert_dims(z, 2, 1)\n",
    "\n",
    "        return z"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e896684",
   "metadata": {},
   "source": [
    "The following cell defines the end-to-end OFDM system with superimposed pilots.\n",
    " \n",
    "Let $N$ denote the number of subcarriers and $M$ the number of OFDM symbols.\n",
    "The transmitter implementation follows the architecture shown in the figure below. Coded bits are first mapped to a QAM constellation and then arranged into a resource grid $\\mathbf{X}_{\\text{d}}$ that *contains only data symbols (no pilots)*.\n",
    "A separate resource grid that contains fixed QPSK pilot symbols is then *superimposed* onto the data resource grid $\\mathbf{X}_{\\text{d}}$ according to:\n",
    "\n",
    "\\begin{equation}\n",
    "\\mathbf{X} = \\sqrt{1-\\alpha} \\mathbf{X}_{\\text{d}} + \\sqrt{\\alpha}\\mathbf{X}_{\\text{p}}\n",
    "\\end{equation}\n",
    "\n",
    "where $\\boldsymbol{\\alpha} \\in \\mathbb{R}^{N \\times M}$ is a *trainable* power allocation matrix such that $0 \\leq \\alpha_{n,m} \\leq 1$ for all $n = 1,\\ldots,N$ and $m = 1,\\ldots,M$. Each element $\\alpha_{n,m}$ represents the fraction of energy allocated to pilots at resource element $(n,m)$. The square roots and products in the equation are applied element-wise."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31153988",
   "metadata": {},
   "source": [
    "![sip notebook.png](attachment:08818eeb-b1c9-4d7c-a82a-0576fcefcbcd.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c0a13ad",
   "metadata": {},
   "source": [
    "Training of the end-to-end system is done on the bit-metric decoding (BMD) rate [3] which is computed from the transmitted bits and LLRs:\n",
    "\n",
    "\\begin{equation}\n",
    "R = 1 - \\frac{1}{SNMK} \\sum_{s = 0}^{S-1} \\sum_{n = 0}^{N-1} \\sum_{m = 0}^{M-1} \\sum_{k = 0}^{K-1} \\texttt{BCE} \\left( B_{s,n,m,k}, \\texttt{LLR}_{s,n,m,k} \\right)\n",
    "\\end{equation}\n",
    "\n",
    "where\n",
    "\n",
    "* $S$ is the batch size\n",
    "* $K$ the number of bits per symbol\n",
    "* $B_{s,n,m,k}$ the $k^{th}$ coded bit transmitted on the resource element $(n,m)$ and for the $s^{th}$ batch example\n",
    "* $\\texttt{LLR}_{s,n,m,k}$ the LLR (logit) computed by the neural receiver corresponding to the $k^{th}$ coded bit transmitted on the resource element $(n,m)$ and for the $s^{th}$ batch example\n",
    "* $\\texttt{BCE} \\left( \\cdot, \\cdot \\right)$ the binary cross-entropy in log base 2\n",
    "\n",
    "Because no outer code is required at training, the outer encoder and decoder are not used at training to reduce computational complexity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c5d2605f-3b5f-481e-b5d1-48e638b9afd1",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-08-06T22:00:39.405123Z"
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   "outputs": [],
   "source": [
    "class OFDMSystemNeuralReceiver(Block):\n",
    "    \"\"\"\n",
    "    End-to-end OFDM system with neural receiver and superimposed pilots.\n",
    "\n",
    "    When training=True, returns the BMD rate loss for training.\n",
    "    When training=False, returns (bits, bits_hat) for BER evaluation.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, training: bool = False):\n",
    "        super().__init__()\n",
    "        self._training = training\n",
    "\n",
    "        n = int(RESOURCE_GRID_0P.num_data_symbols * NUM_BITS_PER_SYMBOL)\n",
    "        k = int(n * CODERATE)\n",
    "        self._k = k\n",
    "        self._n = n\n",
    "\n",
    "        # Transmitter components\n",
    "        self._binary_source = BinarySource()\n",
    "        if not training:\n",
    "            self._encoder = LDPC5GEncoder(k, n)\n",
    "        self._mapper = Mapper(\"qam\", NUM_BITS_PER_SYMBOL)\n",
    "        self._rg_mapper = ResourceGridMapper(RESOURCE_GRID_0P)\n",
    "\n",
    "        # Channel\n",
    "        self._channel = OFDMChannel(\n",
    "            CDL_CHANNEL,\n",
    "            RESOURCE_GRID_0P,\n",
    "            add_awgn=True,\n",
    "            normalize_channel=True,\n",
    "            return_channel=False,\n",
    "        )\n",
    "\n",
    "        # Neural receiver\n",
    "        self._neural_receiver = NeuralReceiver(\n",
    "            NUM_CONV_CHANNELS,\n",
    "            NUM_BITS_PER_SYMBOL,\n",
    "            NUM_OFDM_SYMBOLS,\n",
    "            FFT_SIZE,\n",
    "        ).to(device)\n",
    "\n",
    "        # Resource grid demapper\n",
    "        self._rg_demapper = ResourceGridDemapper(RESOURCE_GRID_0P, STREAM_MANAGEMENT)\n",
    "\n",
    "        # Decoder (only for evaluation)\n",
    "        if not training:\n",
    "            self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n",
    "\n",
    "        # Generate superimposed pilots\n",
    "        # A fixed resource grid of superimposed pilots is randomly generated\n",
    "        # by sampling a QPSK constellation\n",
    "        pilot_source = QAMSource(num_bits_per_symbol=2, seed=1)\n",
    "        pilots = pilot_source([1, 1, 1, RESOURCE_GRID_0P.num_ofdm_symbols, RESOURCE_GRID_0P.fft_size])\n",
    "        # Center the pilots (remove DC component)\n",
    "        mean = torch.mean(pilots)\n",
    "        pilots = pilots - mean\n",
    "        # Normalize the pilots\n",
    "        norm_factor = torch.sqrt(torch.mean(torch.abs(pilots) ** 2))\n",
    "        pilots = pilots / norm_factor\n",
    "        # Move to device and register as buffer (not trainable, but saved with state_dict)\n",
    "        self.register_buffer(\"_pilots\", pilots.to(device))\n",
    "\n",
    "        # Trainable variable that controls the amount of energy allocated to the pilots\n",
    "        # We train its logits, i.e., the actual scaling is obtained by applying a sigmoid\n",
    "        self._scaling_logit = nn.Parameter(torch.randn(pilots.shape, device=device))\n",
    "\n",
    "    def forward(self, batch_size: int, ebno_db: torch.Tensor):\n",
    "        # Compute noise power\n",
    "        no = ebnodb2no(\n",
    "            ebno_db,\n",
    "            num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "            coderate=CODERATE,\n",
    "            resource_grid=RESOURCE_GRID_0P,\n",
    "        )\n",
    "\n",
    "        # Ensure no has shape [batch_size]\n",
    "        if no.dim() == 0:\n",
    "            no = no.expand(batch_size)\n",
    "\n",
    "        # Transmitter\n",
    "        if self._training:\n",
    "            # Skip outer coding during training\n",
    "            codewords = self._binary_source([batch_size, 1, 1, self._n])\n",
    "        else:\n",
    "            bits = self._binary_source([batch_size, 1, 1, self._k])\n",
    "            codewords = self._encoder(bits)\n",
    "\n",
    "        # Map data to constellation symbols\n",
    "        x = self._mapper(codewords)\n",
    "        # Map symbols to resource grid\n",
    "        x_data = self._rg_mapper(x)\n",
    "\n",
    "        # Add superimposed pilots\n",
    "        scaling = torch.sigmoid(self._scaling_logit)\n",
    "        scaling = torch.complex(scaling, torch.zeros_like(scaling))\n",
    "        x_rg = torch.sqrt(1.0 - scaling) * x_data + torch.sqrt(scaling) * self._pilots\n",
    "\n",
    "        # Channel\n",
    "        no_expanded = expand_to_rank(no, x_rg.ndim)\n",
    "        y = self._channel(x_rg, no_expanded)\n",
    "\n",
    "        # Receiver\n",
    "        llr = self._neural_receiver(y, no)\n",
    "        llr = self._rg_demapper(llr)  # Extract data-carrying resource elements\n",
    "        llr = llr.reshape(batch_size, 1, 1, self._n)\n",
    "\n",
    "        if self._training:\n",
    "            # Compute BCE loss for BMD rate\n",
    "            bce = F.binary_cross_entropy_with_logits(\n",
    "                llr, codewords.float(), reduction=\"none\"\n",
    "            )\n",
    "            bce = bce.mean()\n",
    "            # BMD rate\n",
    "            rate = 1.0 - bce / math.log(2.0)\n",
    "            return rate\n",
    "        else:\n",
    "            # Decode\n",
    "            bits_hat = self._decoder(llr)\n",
    "            return bits, bits_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4982feda-3b93-46dc-a936-bd225d7260dd",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "597a8a07",
   "metadata": {},
   "source": [
    "The following cell implements a training loop that performs stochastic gradient descent (SGD) optimization using PyTorch.\n",
    "\n",
    "Each training iteration consists of:\n",
    "- Sampling a batch of signal-to-noise ratios $E_b/N_0$, where $E_b$ is the energy per bit and $N_0$ is the noise variance\n",
    "- Executing a forward pass through the end-to-end system\n",
    "- Computing gradients using PyTorch's autograd and applying them via the Adam optimizer\n",
    "- Periodically displaying the current BMD rate to monitor training progress\n",
    " \n",
    "Upon completion, the trained model weights are saved to a PyTorch checkpoint using [`torch.save`](https://docs.pytorch.org/docs/stable/generated/torch.save.html).\n",
    " \n",
    "**Note:** Training may require significant computational time. For convenience, [pre-trained weights are available for download](https://drive.google.com/file/d/1GAgLXQxpqcVb1skHKLYPsWM4P-N3XqEV/view?usp=sharing). Training is disabled by default in the next cell; set `training = True` to train the model from scratch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c53960db-28ee-4f33-8840-7b1373348162",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-08-06T22:00:39.410492Z"
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   "outputs": [],
   "source": [
    "training = False # Change to True to train your own model\n",
    "if training:\n",
    "    # Instantiating the end-to-end model for training\n",
    "    model = torch.compile(OFDMSystemNeuralReceiver(training=True), mode=\"default\")\n",
    "\n",
    "    # Number of iterations used for training\n",
    "    NUM_TRAINING_ITERATIONS = 5000\n",
    "\n",
    "    # Adam optimizer (SGD variant)\n",
    "    optimizer = torch.optim.Adam(model.parameters())\n",
    "\n",
    "    # Training loop\n",
    "    for i in range(NUM_TRAINING_ITERATIONS):\n",
    "        # Sample a batch of SNRs\n",
    "        ebno_db = torch.empty(BATCH_SIZE, device=device).uniform_(EBN0_DB_MIN, EBN0_DB_MAX)\n",
    "\n",
    "        # Forward pass\n",
    "        optimizer.zero_grad()\n",
    "        rate = model(BATCH_SIZE, ebno_db)\n",
    "\n",
    "        # Loss is negative rate (we maximize rate by minimizing negative rate)\n",
    "        loss = -rate\n",
    "\n",
    "        # Backward pass\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # Print progress\n",
    "        if i % 100 == 0:\n",
    "            print(f\"{i}/{NUM_TRAINING_ITERATIONS}  Rate: {rate.item():.2E}\", end=\"\\r\")\n",
    "\n",
    "    # Save the weights in a file (use _orig_mod to get unwrapped model from torch.compile)\n",
    "    torch.save(model._orig_mod.state_dict(), 'weights-ofdm-sip.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c374949",
   "metadata": {},
   "source": [
    "## Evaluation of the End-to-end System with Superimposed Pilots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc192a74",
   "metadata": {},
   "source": [
    "The next cell implements two baseline systems for comparison: one that assumes perfect knowledge of the channel coefficients, and another that uses least-squares (LS) channel estimation. Both systems employ linear minimum mean square error (LMMSE) equalization with \"app\" (a posteriori probability) demapping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "22f04eb2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T22:00:39.413143Z",
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     "shell.execute_reply": "2026-08-06T22:00:39.417279Z"
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   },
   "outputs": [],
   "source": [
    "class OFDMSystem(Block):\n",
    "    \"\"\"\n",
    "    Baseline OFDM system with conventional channel estimation.\n",
    "\n",
    "    When perfect_csi=True, uses perfect channel state information.\n",
    "    When perfect_csi=False, uses LS channel estimation.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, perfect_csi: bool = False):\n",
    "        super().__init__()\n",
    "        self._perfect_csi = perfect_csi\n",
    "\n",
    "        n = int(RESOURCE_GRID_2P.num_data_symbols * NUM_BITS_PER_SYMBOL)\n",
    "        k = int(n * CODERATE)\n",
    "        self._k = k\n",
    "\n",
    "        # Transmitter components\n",
    "        self._binary_source = BinarySource()\n",
    "        self._encoder = LDPC5GEncoder(k, n)\n",
    "        self._mapper = Mapper(\"qam\", NUM_BITS_PER_SYMBOL)\n",
    "        self._rg_mapper = ResourceGridMapper(RESOURCE_GRID_2P)\n",
    "\n",
    "        # Channel\n",
    "        self._channel = OFDMChannel(\n",
    "            CDL_CHANNEL,\n",
    "            RESOURCE_GRID_2P,\n",
    "            add_awgn=True,\n",
    "            normalize_channel=True,\n",
    "            return_channel=True,\n",
    "        )\n",
    "\n",
    "        # Receiver components\n",
    "        if perfect_csi:\n",
    "            self._removed_null_subc = RemoveNulledSubcarriers(RESOURCE_GRID_2P)\n",
    "        else:\n",
    "            self._ls_est = LSChannelEstimator(RESOURCE_GRID_2P, interpolation_type=\"nn\")\n",
    "\n",
    "        self._lmmse_equ = LMMSEEqualizer(RESOURCE_GRID_2P, STREAM_MANAGEMENT)\n",
    "        self._demapper = Demapper(\"app\", \"qam\", NUM_BITS_PER_SYMBOL)\n",
    "        self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n",
    "\n",
    "    def forward(self, batch_size: int, ebno_db: torch.Tensor):\n",
    "        # Compute noise power\n",
    "        no = ebnodb2no(\n",
    "            ebno_db,\n",
    "            num_bits_per_symbol=NUM_BITS_PER_SYMBOL,\n",
    "            coderate=CODERATE,\n",
    "            resource_grid=RESOURCE_GRID_2P,\n",
    "        )\n",
    "\n",
    "        # Transmitter\n",
    "        bits = self._binary_source([batch_size, 1, RESOURCE_GRID_2P.num_streams_per_tx, self._k])\n",
    "        codewords = self._encoder(bits)\n",
    "        x = self._mapper(codewords)\n",
    "        x_rg = self._rg_mapper(x)\n",
    "\n",
    "        # Channel\n",
    "        no_expanded = expand_to_rank(no, x_rg.ndim)\n",
    "        y, h_freq = self._channel(x_rg, no_expanded)\n",
    "\n",
    "        # Receiver\n",
    "        if self._perfect_csi:\n",
    "            h_hat = self._removed_null_subc(h_freq)\n",
    "            err_var = 0.0\n",
    "        else:\n",
    "            h_hat, err_var = self._ls_est(y, no)\n",
    "\n",
    "        x_hat, no_eff = self._lmmse_equ(y, h_hat, err_var, no)\n",
    "        no_eff_expanded = expand_to_rank(no_eff, x_hat.ndim)\n",
    "        llr = self._demapper(x_hat, no_eff_expanded)\n",
    "        bits_hat = self._decoder(llr)\n",
    "\n",
    "        return bits, bits_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2226b31a",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-08-06T22:01:21.564272Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 3.4993e-01 | 1.0000e+00 |       40850 |      116736 |          128 |         128 |         0.3 |reached target block errors\n",
      "    0.526 | 3.3491e-01 | 1.0000e+00 |       39096 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    1.053 | 3.1774e-01 | 1.0000e+00 |       37092 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    1.579 | 3.0536e-01 | 1.0000e+00 |       35647 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    2.105 | 2.8666e-01 | 1.0000e+00 |       33463 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    2.632 | 2.7177e-01 | 1.0000e+00 |       31725 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    3.158 | 2.4590e-01 | 1.0000e+00 |       28705 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    3.684 | 2.2577e-01 | 1.0000e+00 |       26356 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    4.211 | 1.9681e-01 | 1.0000e+00 |       22975 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    4.737 | 1.5938e-01 | 9.9219e-01 |       18605 |      116736 |          127 |         128 |         0.0 |reached target block errors\n",
      "    5.263 | 8.3787e-02 | 6.9141e-01 |       19562 |      233472 |          177 |         256 |         0.1 |reached target block errors\n",
      "    5.789 | 4.2409e-02 | 3.3854e-01 |       14852 |      350208 |          130 |         384 |         0.1 |reached target block errors\n",
      "    6.316 | 2.1191e-02 | 1.6719e-01 |       12369 |      583680 |          107 |         640 |         0.1 |reached target block errors\n",
      "    6.842 | 1.4241e-02 | 8.7674e-02 |       14962 |     1050624 |          101 |        1152 |         0.3 |reached target block errors\n",
      "    7.368 | 7.4504e-03 | 5.3125e-02 |       13046 |     1751040 |          102 |        1920 |         0.4 |reached target block errors\n",
      "    7.895 | 6.4119e-03 | 3.6577e-02 |       16467 |     2568192 |          103 |        2816 |         0.7 |reached target block errors\n",
      "    8.421 | 3.2397e-03 | 1.8350e-02 |       16262 |     5019648 |          101 |        5504 |         1.3 |reached target block errors\n",
      "    8.947 | 1.8396e-03 | 1.1894e-02 |       14388 |     7821312 |          102 |        8576 |         2.0 |reached target block errors\n",
      "    9.474 | 1.7323e-03 | 9.7656e-03 |       16178 |     9338880 |          100 |       10240 |         2.4 |reached target block errors\n",
      "     10.0 | 1.4010e-03 | 7.4219e-03 |       16355 |    11673600 |           95 |       12800 |         3.0 |reached max iterations\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 2.3131e-01 | 1.0000e+00 |       27002 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    0.526 | 2.1529e-01 | 1.0000e+00 |       25132 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    1.053 | 1.8043e-01 | 1.0000e+00 |       21063 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    1.579 | 1.3573e-01 | 1.0000e+00 |       15845 |      116736 |          128 |         128 |         0.0 |reached target block errors\n",
      "    2.105 | 7.1165e-02 | 6.8359e-01 |       16615 |      233472 |          175 |         256 |         0.1 |reached target block errors\n",
      "    2.632 | 2.7564e-02 | 2.6758e-01 |       12871 |      466944 |          137 |         512 |         0.1 |reached target block errors\n",
      "    3.158 | 1.7485e-02 | 1.4323e-01 |       12247 |      700416 |          110 |         768 |         0.2 |reached target block errors\n",
      "    3.684 | 8.2465e-03 | 5.4167e-02 |       14440 |     1751040 |          104 |        1920 |         0.4 |reached target block errors\n",
      "    4.211 | 5.0072e-03 | 3.6345e-02 |       13444 |     2684928 |          107 |        2944 |         0.7 |reached target block errors\n",
      "    4.737 | 2.9879e-03 | 2.2321e-02 |       12208 |     4085760 |          100 |        4480 |         1.0 |reached target block errors\n",
      "    5.263 | 1.6268e-03 | 1.0663e-02 |       14053 |     8638464 |          101 |        9472 |         2.2 |reached target block errors\n",
      "    5.789 | 1.0670e-03 | 7.7344e-03 |       12456 |    11673600 |           99 |       12800 |         2.9 |reached max iterations\n",
      "    6.316 | 6.9225e-04 | 4.9219e-03 |        8081 |    11673600 |           63 |       12800 |         2.9 |reached max iterations\n",
      "    6.842 | 5.1509e-04 | 3.7500e-03 |        6013 |    11673600 |           48 |       12800 |         2.9 |reached max iterations\n",
      "    7.368 | 2.1810e-04 | 1.5625e-03 |        2546 |    11673600 |           20 |       12800 |         2.9 |reached max iterations\n",
      "    7.895 | 2.3874e-04 | 1.4062e-03 |        2787 |    11673600 |           18 |       12800 |         2.9 |reached max iterations\n",
      "    8.421 | 1.1847e-04 | 8.5938e-04 |        1383 |    11673600 |           11 |       12800 |         2.9 |reached max iterations\n",
      "    8.947 | 1.5522e-04 | 1.0156e-03 |        1812 |    11673600 |           13 |       12800 |         2.9 |reached max iterations\n",
      "    9.474 | 5.4996e-05 | 3.1250e-04 |         642 |    11673600 |            4 |       12800 |         2.9 |reached max iterations\n",
      "     10.0 | 6.1849e-05 | 5.4688e-04 |         722 |    11673600 |            7 |       12800 |         2.9 |reached max iterations\n"
     ]
    }
   ],
   "source": [
    "ber_plots = PlotBER(\"Advanced neural receiver\")\n",
    "\n",
    "baseline_ls = OFDMSystem(False)\n",
    "ber_plots.simulate(baseline_ls,\n",
    "                  ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 20),\n",
    "                  batch_size=BATCH_SIZE,\n",
    "                  num_target_block_errors=100, # simulate until 100 block errors occurred\n",
    "                  legend=\"Baseline: LS Estimation\",\n",
    "                  max_mc_iter=100, # run 100 Monte-Carlo simulations (each with batch_size samples)\n",
    "                  verbose=True,\n",
    "                  show_fig=False);\n",
    "\n",
    "baseline_pcsi = OFDMSystem(True)\n",
    "ber_plots.simulate(baseline_pcsi,\n",
    "                  ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 20),\n",
    "                  batch_size=BATCH_SIZE,\n",
    "                  num_target_block_errors=100, # simulate until 100 block errors occurred\n",
    "                  legend=\"Baseline: Perfect CSI\",\n",
    "                  max_mc_iter=100, # run 100 Monte-Carlo simulations (each with batch_size samples)\n",
    "                  verbose=True,\n",
    "                  show_fig=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3de79d49",
   "metadata": {},
   "source": [
    "Next, we instantiate the end-to-end system with the neural receiver and evaluate its performance. We also visualize the learned energy allocation matrix $\\mathbf{\\alpha}$ by comparing it before and after training. The initial matrix represents random initialization, while the trained matrix shows the optimized energy distribution between pilots and data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6a0e6e6b-3650-4524-9c83-46c715493e3f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T22:01:21.568083Z",
     "iopub.status.busy": "2026-08-06T22:01:21.567945Z",
     "iopub.status.idle": "2026-08-06T22:01:22.143154Z",
     "shell.execute_reply": "2026-08-06T22:01:22.142287Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiating the end-to-end model for evaluation\n",
    "model = OFDMSystemNeuralReceiver(training=False)\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.imshow(torch.sigmoid(model._scaling_logit[0,0,0]).detach().cpu().numpy())\n",
    "plt.colorbar(label=\"Ratio of energy allocated to pilots\", orientation=\"horizontal\")\n",
    "plt.title(\"Initialization\")\n",
    "\n",
    "# Run one inference to initialize the model\n",
    "model(1, torch.tensor(10.0, device=device))\n",
    "\n",
    "# Load the trained weights (strict=False because training model doesn't have encoder/decoder)\n",
    "model.load_state_dict(torch.load('weights-ofdm-sip.pt', weights_only=True), strict=False)\n",
    "    \n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.imshow(torch.sigmoid(model._scaling_logit[0,0,0]).detach().cpu().numpy())\n",
    "plt.colorbar(label=\"Ratio of energy allocated to pilots\", orientation=\"horizontal\")\n",
    "plt.title(\"Trained\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "442fe049",
   "metadata": {},
   "source": [
    "Finally, we evaluate the performance of our end-to-end system with superimposed pilots and the neural receiver and visualize the bit error rate (BER) curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fc0e47a5-7d61-4e8b-b077-8fc63a33057f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-06T22:01:22.145197Z",
     "iopub.status.busy": "2026-08-06T22:01:22.145055Z",
     "iopub.status.idle": "2026-08-06T22:02:06.758031Z",
     "shell.execute_reply": "2026-08-06T22:02:06.757165Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 2.3932e-01 | 1.0000e+00 |       32594 |      136192 |          128 |         128 |         0.1 |reached target block errors\n",
      "    0.526 | 2.1957e-01 | 1.0000e+00 |       29904 |      136192 |          128 |         128 |         0.0 |reached target block errors\n",
      "    1.053 | 1.9515e-01 | 1.0000e+00 |       26578 |      136192 |          128 |         128 |         0.0 |reached target block errors\n",
      "    1.579 | 1.5097e-01 | 1.0000e+00 |       20561 |      136192 |          128 |         128 |         0.0 |reached target block errors\n",
      "    2.105 | 8.8853e-02 | 7.3828e-01 |       24202 |      272384 |          189 |         256 |         0.1 |reached target block errors\n",
      "    2.632 | 5.0921e-02 | 3.9844e-01 |       13870 |      272384 |          102 |         256 |         0.1 |reached target block errors\n",
      "    3.158 | 2.5828e-02 | 2.0703e-01 |       14070 |      544768 |          106 |         512 |         0.2 |reached target block errors\n",
      "    3.684 | 1.9384e-02 | 1.3542e-01 |       15840 |      817152 |          104 |         768 |         0.3 |reached target block errors\n",
      "    4.211 | 1.0157e-02 | 7.1733e-02 |       15217 |     1498112 |          101 |        1408 |         0.5 |reached target block errors\n",
      "    4.737 | 6.5588e-03 | 5.0781e-02 |       14292 |     2179072 |          104 |        2048 |         0.8 |reached target block errors\n",
      "    5.263 | 2.9570e-03 | 2.2545e-02 |       14095 |     4766720 |          101 |        4480 |         1.7 |reached target block errors\n",
      "    5.789 | 2.0919e-03 | 1.5319e-02 |       14530 |     6945792 |          100 |        6528 |         2.4 |reached target block errors\n",
      "    6.316 | 1.0309e-03 | 8.6806e-03 |       12636 |    12257280 |          100 |       11520 |         4.3 |reached target block errors\n",
      "    6.842 | 1.0212e-03 | 7.1875e-03 |       13908 |    13619200 |           92 |       12800 |         4.8 |reached max iterations\n",
      "    7.368 | 6.5459e-04 | 4.8437e-03 |        8915 |    13619200 |           62 |       12800 |         4.8 |reached max iterations\n",
      "    7.895 | 5.0502e-04 | 3.7500e-03 |        6878 |    13619200 |           48 |       12800 |         4.8 |reached max iterations\n",
      "    8.421 | 3.0229e-04 | 2.1094e-03 |        4117 |    13619200 |           27 |       12800 |         4.8 |reached max iterations\n",
      "    8.947 | 2.3907e-04 | 1.6406e-03 |        3256 |    13619200 |           21 |       12800 |         4.8 |reached max iterations\n",
      "    9.474 | 1.1763e-04 | 7.0312e-04 |        1602 |    13619200 |            9 |       12800 |         4.8 |reached max iterations\n",
      "     10.0 | 1.7644e-04 | 1.2500e-03 |        2403 |    13619200 |           16 |       12800 |         4.8 |reached max iterations\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Computing and plotting BER\n",
    "ber_plots.simulate(model,\n",
    "                   ebno_dbs=np.linspace(EBN0_DB_MIN, EBN0_DB_MAX, 20),\n",
    "                   batch_size=BATCH_SIZE,\n",
    "                   num_target_block_errors=100,\n",
    "                   legend=\"SIP + Neural receiver\",\n",
    "                   max_mc_iter=100,\n",
    "                   verbose=True,\n",
    "                   show_fig=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48182e7d",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c51d8f6",
   "metadata": {},
   "source": [
    "[1] F. Ait Aoudia and J. Hoydis, \"End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication,\" in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2021.3101364.\n",
    "\n",
    "[2] M. Honkala, D. Korpi and J. M. J. Huttunen, \"DeepRx: Fully Convolutional Deep Learning Receiver,\" in IEEE Transactions on Wireless Communications, vol. 20, no. 6, pp. 3925-3940, June 2021, doi: 10.1109/TWC.2021.3054520.\n",
    "\n",
    "[3] G. Böcherer, \"Achievable Rates for Probabilistic Shaping\", arXiv:1707.01134, 2017."
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "widgets": {
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