{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb1af397-500c-4a88-8168-abc57a1aa513",
   "metadata": {},
   "source": [
    "# End-to-end Learning with Autoencoders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79b1ed0-1dcc-4a62-a9dd-09c13eec0b80",
   "metadata": {},
   "source": [
    "In this notebook, you will learn how to implement an end-to-end communication system as an autoencoder [1].\n",
    "The implemented system is shown in the figure below.\n",
    "An additive white Gaussian noise (AWGN) channel is considered.\n",
    "On the transmitter side, joint training of the constellation geometry and bit-labeling is performed, as in [2].\n",
    "On the receiver side, a neural network-based demapper that computes log-likelihood ratios (LLRs) on the transmitted bits from the received samples is optimized.\n",
    "The considered autoencoder is benchmarked against a quadrature amplitude modulation (QAM) with Gray labeling and the optimal AWGN demapper."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4694269-5aee-4623-9ab0-6a9f84d9956c",
   "metadata": {},
   "source": [
    "![System Model](data:image/png;base64,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)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2920adb0-9dd0-484d-9496-cfbf87af5eaa",
   "metadata": {},
   "source": [
    "Two algorithms for training the autoencoder are implemented in this notebook:\n",
    "\n",
    "* Conventional stochastic gradient descent (SGD) with backpropagation, which assumes a differentiable channel model and therefore optimizes the end-to-end system by backpropagating the gradients through the channel (see, e.g., [1]).\n",
    "* The training algorithm from [3], which does not assume a differentiable channel model, and which trains the end-to-end system by alternating between conventional training of the receiver and reinforcement learning (RL)-based training of the transmitter. Compared to [3], an additional step of fine-tuning of the receiver is performed after alternating training.\n",
    "\n",
    "**Note:** For an introduction to the implementation of differentiable communication systems and their optimization through SGD and backpropagation with Sionna, please refer to [the Part 2 of the Sionna tutorial for Beginners](https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Sionna_tutorial_part2.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3287acf2-0dcd-419f-8cfc-27ea29ca3af6",
   "metadata": {},
   "source": [
    "* [Configuration and Imports](#Configuration-and-Imports)\n",
    "* [Simulation Parameters](#Simulation-Parameters)\n",
    "* [Neural Demapper](#Neural-Demapper)\n",
    "* [Trainable End-to-end System: Conventional Training](#Trainable-End-to-end-System:-Conventional-Training)\n",
    "* [Trainable End-to-end System: RL-based Training](#Trainable-End-to-end-System:-RL-based-Training)\n",
    "* [Evaluation](#Evaluation)\n",
    "* [Visualizing the Learned Constellations](#Visualizing-the-Learned-Constellations)\n",
    "* [References](#References)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d35352-0937-4710-9f2c-7e50f819ea2c",
   "metadata": {},
   "source": [
    "## Configuration and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e201e1d-e0d8-4133-b251-14823c25f68b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:10:28.589158Z",
     "iopub.status.busy": "2026-02-13T14:10:28.588957Z",
     "iopub.status.idle": "2026-02-13T14:10:31.407668Z",
     "shell.execute_reply": "2026-02-13T14:10:31.406517Z"
    }
   },
   "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 pickle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch._dynamo\n",
    "\n",
    "from sionna.phy import Block\n",
    "from sionna.phy.channel import AWGN\n",
    "from sionna.phy.utils import ebnodb2no, expand_to_rank, sim_ber\n",
    "from sionna.phy.fec.ldpc import LDPC5GEncoder, LDPC5GDecoder\n",
    "from sionna.phy.mapping import Mapper, Demapper, Constellation, BinarySource\n",
    "\n",
    "sionna.phy.config.seed = 42  # Set seed for reproducible random number generation\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca65b28-74a4-4cd9-8c66-748259d05e57",
   "metadata": {},
   "source": [
    "## Simulation Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ddfd420b-0f45-468a-b292-8aaea2ce280f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:10:31.410196Z",
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   "source": [
    "###############################################\n",
    "# SNR range for evaluation and training [dB]\n",
    "###############################################\n",
    "ebno_db_min = 4.0\n",
    "ebno_db_max = 8.0\n",
    "\n",
    "###############################################\n",
    "# Modulation and coding configuration\n",
    "###############################################\n",
    "num_bits_per_symbol = 6  # Baseline is 64-QAM\n",
    "modulation_order = 2**num_bits_per_symbol\n",
    "coderate = 0.5  # Coderate for the outer code\n",
    "n = 1500  # Codeword length [bit]. Must be a multiple of num_bits_per_symbol\n",
    "num_symbols_per_codeword = n // num_bits_per_symbol  # Number of modulated baseband symbols per codeword\n",
    "k = int(n * coderate)  # Number of information bits per codeword\n",
    "\n",
    "###############################################\n",
    "# Training configuration\n",
    "###############################################\n",
    "num_training_iterations_conventional = 10000  # Number of training iterations for conventional training\n",
    "# Number of training iterations with RL-based training for the alternating training phase and fine-tuning of the receiver phase\n",
    "num_training_iterations_rl_alt = 7000\n",
    "num_training_iterations_rl_finetuning = 3000\n",
    "training_batch_size = 128  # Training batch size\n",
    "rl_perturbation_var = 0.01  # Variance of the perturbation used for RL-based training of the transmitter\n",
    "model_weights_path_conventional_training = \"awgn_autoencoder_weights_conventional_training\"  # Filename to save the autoencoder weights once conventional training is done\n",
    "model_weights_path_rl_training = \"awgn_autoencoder_weights_rl_training\"  # Filename to save the autoencoder weights once RL-based training is done\n",
    "\n",
    "###############################################\n",
    "# Evaluation configuration\n",
    "###############################################\n",
    "results_filename = \"awgn_autoencoder_results\"  # Location to save the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d711f03c-93b9-4524-a7a6-63a35b825623",
   "metadata": {},
   "source": [
    "## Neural Demapper"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caff5b7e-0ae4-4890-a45b-378afc5bf9a5",
   "metadata": {},
   "source": [
    "The neural network-based demapper shown in the figure above is made of three dense layers with ReLU activation.\n",
    "\n",
    "The input of the demapper consists of a received sample $y \\in \\mathbb{C}$ and the noise power spectral density $N_0$ in log-10 scale to handle different orders of magnitude for the SNR.\n",
    "\n",
    "As the neural network can only process real-valued inputs, these values are fed as a 3-dimensional vector\n",
    "\n",
    "$$\\left[ \\mathcal{R}(y), \\mathcal{I}(y), \\log_{10}(N_0) \\right]$$\n",
    "\n",
    "where $\\mathcal{R}(y)$ and $\\mathcal{I}(y)$ refer to the real and imaginary component of $y$, respectively.\n",
    "\n",
    "The output of the neural network-based demapper consists of LLRs on the `num_bits_per_symbol` bits mapped to a constellation point. Therefore, the last layer consists of ``num_bits_per_symbol`` units.\n",
    "\n",
    "**Note**: The neural network-based demapper processes the received samples $y$ forming a block individually. The [neural receiver notebook](https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Neural_Receiver.html) provides an example of a more advanced neural network-based receiver that jointly processes a resource grid of received symbols."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e636efd-9a46-4fd1-8790-b4cb1b63f31e",
   "metadata": {
    "execution": {
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   "source": [
    "class NeuralDemapper(nn.Module):\n",
    "    \"\"\"Neural network-based demapper with three dense layers and ReLU activation.\n",
    "    \n",
    "    The input of the demapper consists of a received sample y ∈ ℂ and the noise\n",
    "    variance. The output is a vector of LLRs for each bit carried by a symbol.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        \n",
    "        self._dense_1 = nn.Linear(3, 128)\n",
    "        self._dense_2 = nn.Linear(128, 128)\n",
    "        self._dense_3 = nn.Linear(128, num_bits_per_symbol)  # Output LLRs for every bit\n",
    "\n",
    "    def forward(self, y, no):\n",
    "        # Using log10 scale helps with the performance\n",
    "        no_db = torch.log10(no)\n",
    "        \n",
    "        # Stacking the real and imaginary components of the complex received samples\n",
    "        # and the noise variance\n",
    "        no_db = no_db.expand(-1, num_symbols_per_codeword)  # [batch size, num_symbols_per_codeword]\n",
    "        z = torch.stack([y.real, y.imag, no_db], dim=2)  # [batch size, num_symbols_per_codeword, 3]\n",
    "        \n",
    "        llr = F.relu(self._dense_1(z))\n",
    "        llr = F.relu(self._dense_2(llr))\n",
    "        llr = self._dense_3(llr)  # [batch size, num_symbols_per_codeword, num_bits_per_symbol]\n",
    "        \n",
    "        return llr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d232019-1c8b-4d51-b125-a1f0a4189dac",
   "metadata": {},
   "source": [
    "## Trainable End-to-end System: Conventional Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4cdeeb8-879c-4ae1-a734-6a04ffa48dd3",
   "metadata": {},
   "source": [
    "The following cell defines an end-to-end communication system that transmits bits modulated using a trainable constellation over an AWGN channel.\n",
    "\n",
    "The receiver uses the previously defined neural network-based demapper to compute LLRs on the transmitted (coded) bits.\n",
    "\n",
    "As in [1], the constellation and neural network-based demapper are jointly trained through SGD and backpropagation using the binary cross entropy (BCE) as loss function.\n",
    "\n",
    "Training on the BCE is known to be equivalent to maximizing an achievable information rate [2].\n",
    "\n",
    "The following model can be instantiated either for training (`training = True`) or evaluation (`training = False`).\n",
    "\n",
    "In the former case, the BCE is returned and no outer code is used to reduce computational complexity and as it does not impact the training of the constellation or demapper.\n",
    "\n",
    "When setting `training` to `False`, an LDPC outer code from 5G NR is applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2709aae3-7d40-4f35-a56f-79bd09a81e56",
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    "execution": {
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   "source": [
    "class E2ESystemConventionalTraining(nn.Module):\n",
    "    \"\"\"End-to-end communication system for conventional training.\n",
    "    \n",
    "    This system transmits bits modulated using a trainable constellation over\n",
    "    an AWGN channel. The receiver uses a neural network-based demapper.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, training):\n",
    "        super().__init__()\n",
    "        \n",
    "        self._training = training\n",
    "        \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        self._binary_source = BinarySource()\n",
    "        # To reduce the computational complexity of training, the outer code is not used when training\n",
    "        if not self._training:\n",
    "            # num_bits_per_symbol is required for the interleaver\n",
    "            self._encoder = LDPC5GEncoder(k, n, num_bits_per_symbol)\n",
    "        \n",
    "        # Trainable constellation\n",
    "        # We initialize a custom constellation with QAM points\n",
    "        qam_points = Constellation(\"qam\", num_bits_per_symbol).points\n",
    "        self.points_r = nn.Parameter(qam_points.real.clone())\n",
    "        self.points_i = nn.Parameter(qam_points.imag.clone())\n",
    "\n",
    "        self.constellation = Constellation(\"custom\",\n",
    "                                           num_bits_per_symbol,\n",
    "                                           points=torch.complex(self.points_r, self.points_i),\n",
    "                                           normalize=True,\n",
    "                                           center=True)\n",
    "       \n",
    "        \n",
    "        self._mapper = Mapper(constellation=self.constellation)\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        self._channel = AWGN()\n",
    "        \n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        # We use the previously defined neural network for demapping\n",
    "        self._demapper = NeuralDemapper()\n",
    "        # To reduce the computational complexity of training, the outer code is not used when training\n",
    "        if not self._training:\n",
    "            self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n",
    "\n",
    "    def forward(self, batch_size, ebno_db):\n",
    "        \n",
    "        # Update constellation points from trainable parameters (creates fresh graph)\n",
    "        self.constellation.points = torch.complex(self.points_r, self.points_i)\n",
    "\n",
    "        # If `ebno_db` is a scalar, a tensor with shape [batch size] is created\n",
    "        if ebno_db.dim() == 0:\n",
    "            ebno_db = ebno_db.expand(batch_size)\n",
    "        no = ebnodb2no(ebno_db, num_bits_per_symbol, coderate)\n",
    "        no = expand_to_rank(no, 2)\n",
    "        \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        # Outer coding is only performed if not training\n",
    "        if self._training:\n",
    "            c = self._binary_source([batch_size, n])\n",
    "        else:\n",
    "            b = self._binary_source([batch_size, k])\n",
    "            c = self._encoder(b)\n",
    "        # Modulation\n",
    "        x = self._mapper(c)  # x [batch size, num_symbols_per_codeword]\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        y = self._channel(x, no)  # [batch size, num_symbols_per_codeword]\n",
    "        \n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        llr = self._demapper(y, no)\n",
    "        llr = llr.reshape(batch_size, n)\n",
    "        # If training, outer decoding is not performed and the BCE is returned\n",
    "        if self._training:\n",
    "            loss = F.binary_cross_entropy_with_logits(llr, c)\n",
    "            return loss\n",
    "        else:\n",
    "            # Outer decoding\n",
    "            b_hat = self._decoder(llr)\n",
    "            return b, b_hat  # Ground truth and reconstructed information bits returned for BER/BLER computation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f347823-c28d-4a00-8a02-f9b5e6d450f3",
   "metadata": {},
   "source": [
    "A simple training loop is defined in the next cell, which performs `num_training_iterations_conventional` training iterations of SGD. Training is done over a range of SNR, by randomly sampling a batch of SNR values at each iteration."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d95542ed-a6be-4c72-bae5-5154ded0340b",
   "metadata": {},
   "source": [
    "**Note:** For an introduction to the implementation of differentiable communication systems and their optimization through SGD and backpropagation with Sionna, please refer to [the Part 2 of the Sionna tutorial for Beginners](https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Sionna_tutorial_part2.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ab429a86-f606-420c-8ad1-e0efc6874584",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-02-13T14:10:31.431601Z"
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   },
   "outputs": [],
   "source": [
    "def conventional_training(model):\n",
    "    \"\"\"Train the model using conventional SGD with backpropagation.\"\"\"\n",
    "    # Optimizer used to apply gradients\n",
    "    optimizer = torch.optim.Adam(model.parameters())\n",
    "    device = sionna.phy.config.device\n",
    "    \n",
    "    for i in range(num_training_iterations_conventional):\n",
    "        optimizer.zero_grad()\n",
    "        # Sampling a batch of SNRs\n",
    "        ebno_db = torch.empty(training_batch_size, device=device).uniform_(ebno_db_min, ebno_db_max)\n",
    "        # Forward pass\n",
    "        loss = model(training_batch_size, ebno_db)\n",
    "        # Computing and applying gradients\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        # Printing periodically the progress\n",
    "        if i % 100 == 0:\n",
    "            print(f'Iteration {i}/{num_training_iterations_conventional}  BCE: {loss.item():.4f}', end='\\r')\n",
    "    print()\n",
    "    model.eval()\n",
    "    optimizer.zero_grad(set_to_none=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5959e24-1107-4271-be5c-944eacdc3033",
   "metadata": {},
   "source": [
    "The next cell defines a utility function for saving the weights using [pickle](https://docs.python.org/3/library/pickle.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f32c0d68-55f1-41e1-88da-28b96c3cac0c",
   "metadata": {
    "execution": {
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   "source": [
    "def save_weights(model, model_weights_path):\n",
    "    \"\"\"Save model weights using torch.save.\"\"\"\n",
    "    torch.save(model._orig_mod.state_dict(), model_weights_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6ad9e4-7df4-4988-bbb4-dedd0ba50f47",
   "metadata": {},
   "source": [
    "In the next cell, an instance of the model defined previously is instantiated and trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5971c6da-52bf-46ab-91f0-57baf560964d",
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    "execution": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0/10000  BCE: 0.6895\r\n",
      "Iteration 200/10000  BCE: 0.3131\r\n",
      "Iteration 400/10000  BCE: 0.3018\r\n",
      "Iteration 600/10000  BCE: 0.2977\r\n",
      "Iteration 800/10000  BCE: 0.2866\r\n",
      "Iteration 1000/10000  BCE: 0.2912\r\n",
      "Iteration 1200/10000  BCE: 0.2883\r\n",
      "Iteration 1400/10000  BCE: 0.2916\r\n",
      "Iteration 1600/10000  BCE: 0.2913\r\n",
      "Iteration 1800/10000  BCE: 0.2848\r\n",
      "Iteration 2000/10000  BCE: 0.2940\r\n",
      "Iteration 2200/10000  BCE: 0.2842\r\n",
      "Iteration 2400/10000  BCE: 0.2842\r\n",
      "Iteration 2600/10000  BCE: 0.2883\r\n",
      "Iteration 2800/10000  BCE: 0.2887\r\n",
      "Iteration 3000/10000  BCE: 0.2886\r\n",
      "Iteration 3200/10000  BCE: 0.2835\r\n",
      "Iteration 3400/10000  BCE: 0.2820\r\n",
      "Iteration 3600/10000  BCE: 0.2819\r\n",
      "Iteration 3800/10000  BCE: 0.2879\r\n",
      "Iteration 4000/10000  BCE: 0.2924\r\n",
      "Iteration 4200/10000  BCE: 0.2925\r\n",
      "Iteration 4400/10000  BCE: 0.2839\r\n",
      "Iteration 4600/10000  BCE: 0.2866\r\n",
      "Iteration 4800/10000  BCE: 0.2897\r\n",
      "Iteration 5000/10000  BCE: 0.2818\r\n",
      "Iteration 5200/10000  BCE: 0.2890\r\n",
      "Iteration 5400/10000  BCE: 0.2833\r\n",
      "Iteration 5600/10000  BCE: 0.2905\r\n",
      "Iteration 5800/10000  BCE: 0.2881\r\n",
      "Iteration 6000/10000  BCE: 0.2852\r\n",
      "Iteration 6200/10000  BCE: 0.2820\r\n",
      "Iteration 6400/10000  BCE: 0.2847\r\n",
      "Iteration 6600/10000  BCE: 0.2911\r\n",
      "Iteration 6800/10000  BCE: 0.2846\r\n",
      "Iteration 7000/10000  BCE: 0.2781\r\n",
      "Iteration 7200/10000  BCE: 0.2818\r\n",
      "Iteration 7400/10000  BCE: 0.2869\r\n",
      "Iteration 7600/10000  BCE: 0.2836\r\n",
      "Iteration 7800/10000  BCE: 0.2814\r\n",
      "Iteration 8000/10000  BCE: 0.2851\r\n",
      "Iteration 8200/10000  BCE: 0.2785\r\n",
      "Iteration 8400/10000  BCE: 0.2884\r\n",
      "Iteration 8600/10000  BCE: 0.2901\r\n",
      "Iteration 8800/10000  BCE: 0.2921\r\n",
      "Iteration 9000/10000  BCE: 0.2863\r\n",
      "Iteration 9200/10000  BCE: 0.2817\r\n",
      "Iteration 9400/10000  BCE: 0.2881\r\n",
      "Iteration 9600/10000  BCE: 0.2924\r\n",
      "Iteration 9800/10000  BCE: 0.2895\r\n",
      "Iteration 9900/10000  BCE: 0.2894\r\n"
     ]
    }
   ],
   "source": [
    "# Instantiate and train the end-to-end system\n",
    "device = sionna.phy.config.device\n",
    "model = torch.compile(E2ESystemConventionalTraining(training=True).to(device), mode=\"reduce-overhead\")\n",
    "conventional_training(model)\n",
    "save_weights(model, model_weights_path_conventional_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e884151-1047-4cb7-b1ee-5610924128a3",
   "metadata": {},
   "source": [
    "## Trainable End-to-end System: RL-based Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d960a61-c6f2-400e-8a6b-ea1c7c2d2db7",
   "metadata": {},
   "source": [
    "The following cell defines the same end-to-end system as before, but stop the gradients after the channel to simulate a non-differentiable channel.\n",
    "\n",
    "To jointly train the transmitter and receiver over a non-differentiable channel, we follow [3], which key idea is to alternate between:\n",
    "\n",
    "- Training of the receiver on the BCE using conventional backpropagation and SGD.\n",
    "- Training of the transmitter by applying (known) perturbations to the transmitter output to enable estimation of the gradient of the transmitter weights with respect to an approximation of the loss function.\n",
    "\n",
    "When `training` is set to `True`, both losses for training the receiver and the transmitter are returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "60f00146-1051-4406-a3cd-4a6e3305b515",
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   "source": [
    "class E2ESystemRLTraining(nn.Module):\n",
    "    \"\"\"End-to-end communication system for RL-based training.\n",
    "    \n",
    "    This system is similar to E2ESystemConventionalTraining but stops gradients\n",
    "    after the channel to simulate a non-differentiable channel. Training uses\n",
    "    reinforcement learning with perturbation-based exploration.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, training):\n",
    "        super().__init__()\n",
    "        \n",
    "        self._training = training\n",
    "        \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        self._binary_source = BinarySource()\n",
    "        # To reduce the computational complexity of training, the outer code is not used when training\n",
    "        if not self._training:\n",
    "            self._encoder = LDPC5GEncoder(k, n, num_bits_per_symbol)\n",
    "        \n",
    "        # Trainable constellation\n",
    "        # We initialize a custom constellation with QAM points\n",
    "        qam_points = Constellation(\"qam\", num_bits_per_symbol).points\n",
    "        self.points_r = nn.Parameter(qam_points.real.clone())\n",
    "        self.points_i = nn.Parameter(qam_points.imag.clone())\n",
    "\n",
    "        self.constellation = Constellation(\"custom\",\n",
    "                                           num_bits_per_symbol,\n",
    "                                           points=torch.complex(self.points_r, self.points_i),\n",
    "                                           normalize=True,\n",
    "                                           center=True)\n",
    "        \n",
    "        self._mapper = Mapper(constellation=self.constellation)\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        self._channel = AWGN()\n",
    "        \n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        # We use the previously defined neural network for demapping\n",
    "        self._demapper = NeuralDemapper()\n",
    "        # To reduce the computational complexity of training, the outer code is not used when training\n",
    "        if not self._training:\n",
    "            self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n",
    "\n",
    "    def forward(self, batch_size, ebno_db, perturbation_variance=0.0):\n",
    "        # If `ebno_db` is a scalar, a tensor with shape [batch size] is created\n",
    "        if ebno_db.dim() == 0:\n",
    "            ebno_db = ebno_db.expand(batch_size)\n",
    "        no = ebnodb2no(ebno_db, num_bits_per_symbol, coderate)\n",
    "        no = expand_to_rank(no, 2)\n",
    "        \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        # Outer coding is only performed if not training\n",
    "        if self._training:\n",
    "            c = self._binary_source([batch_size, n])\n",
    "        else:\n",
    "            b = self._binary_source([batch_size, k])\n",
    "            c = self._encoder(b)\n",
    "        # Modulation\n",
    "        # Update constellation points from trainable parameters\n",
    "        self.constellation.points = torch.complex(self.points_r, self.points_i)\n",
    "        x = self._mapper(c)  # x [batch size, num_symbols_per_codeword]\n",
    "        \n",
    "        # Adding perturbation\n",
    "        # If ``perturbation_variance`` is 0, then the added perturbation is null\n",
    "        std = (0.5 * perturbation_variance) ** 0.5\n",
    "        epsilon_r = torch.randn(x.shape, device=x.device, dtype=x.real.dtype) * std\n",
    "        epsilon_i = torch.randn(x.shape, device=x.device, dtype=x.real.dtype) * std\n",
    "        epsilon = torch.complex(epsilon_r, epsilon_i)  # [batch size, num_symbols_per_codeword]\n",
    "        x_p = x + epsilon  # [batch size, num_symbols_per_codeword]\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        y = self._channel(x_p, no)  # [batch size, num_symbols_per_codeword]\n",
    "        y = y.detach()  # Stop gradient here\n",
    "        \n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        llr = self._demapper(y, no)\n",
    "        \n",
    "        # If training, outer decoding is not performed\n",
    "        if self._training:\n",
    "            # Average BCE for each baseband symbol and each batch example\n",
    "            c_reshaped = c.reshape(-1, num_symbols_per_codeword, num_bits_per_symbol)\n",
    "            bce = F.binary_cross_entropy_with_logits(llr, c_reshaped, reduction='none').mean(dim=2)  # Average over the bits mapped to a same baseband symbol\n",
    "            # The RX loss is the usual average BCE\n",
    "            rx_loss = bce.mean()\n",
    "            # From the TX side, the BCE is seen as a feedback from the RX through which backpropagation is not possible\n",
    "            bce = bce.detach()  # [batch size, num_symbols_per_codeword]\n",
    "            x_p = x_p.detach()\n",
    "            p = x_p - x  # [batch size, num_symbols_per_codeword] Gradient is backpropagated through `x`\n",
    "            tx_loss = p.real.square() + p.imag.square()  # [batch size, num_symbols_per_codeword]\n",
    "            tx_loss = -bce * tx_loss / rl_perturbation_var  # [batch size, num_symbols_per_codeword]\n",
    "            tx_loss = tx_loss.mean()\n",
    "            return tx_loss, rx_loss\n",
    "        else:\n",
    "            llr = llr.reshape(-1, n)  # Reshape as expected by the outer decoder\n",
    "            b_hat = self._decoder(llr)\n",
    "            return b, b_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65a5114-3cf3-4911-9e15-2863f350ba7c",
   "metadata": {},
   "source": [
    "The next cell implements the training algorithm from [3], which alternates between conventional training of the neural network-based receiver, and RL-based training of the transmitter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8368cd0f-6ecf-4ded-a0a8-fc595a518086",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:10:50.822521Z",
     "iopub.status.busy": "2026-02-13T14:10:50.822311Z",
     "iopub.status.idle": "2026-02-13T14:10:50.829276Z",
     "shell.execute_reply": "2026-02-13T14:10:50.828435Z"
    }
   },
   "outputs": [],
   "source": [
    "def rl_based_training(model):\n",
    "    \"\"\"Train the model using RL-based training algorithm.\"\"\"\n",
    "    # Optimizers used to apply gradients\n",
    "    optimizer_tx = torch.optim.Adam(model.parameters())  # For training the transmitter\n",
    "    optimizer_rx = torch.optim.Adam(model.parameters())  # For training the receiver\n",
    "    device = sionna.phy.config.device\n",
    "    \n",
    "    # Training loop\n",
    "    for i in range(num_training_iterations_rl_alt):\n",
    "        # 10 steps of receiver training are performed to keep it ahead of the transmitter\n",
    "        # as it is used for computing the losses when training the transmitter\n",
    "        for _ in range(10):\n",
    "            optimizer_rx.zero_grad()\n",
    "            # Sampling a batch of SNRs\n",
    "            ebno_db = torch.empty(training_batch_size, device=device).uniform_(ebno_db_min, ebno_db_max)\n",
    "            # Forward pass - keep only the RX loss, no perturbation\n",
    "            _, rx_loss = model(training_batch_size, ebno_db)\n",
    "            rx_loss.backward()\n",
    "            optimizer_rx.step()\n",
    "        # One step of transmitter training\n",
    "        optimizer_tx.zero_grad()\n",
    "        # Sampling a batch of SNRs\n",
    "        ebno_db = torch.empty(training_batch_size, device=device).uniform_(ebno_db_min, ebno_db_max)\n",
    "        # Forward pass - keep only the TX loss, perturbations added for RL exploration\n",
    "        tx_loss, _ = model(training_batch_size, ebno_db, rl_perturbation_var)\n",
    "        tx_loss.backward()\n",
    "        optimizer_tx.step()\n",
    "        # Printing periodically the progress\n",
    "        if i % 100 == 0:\n",
    "            print(f'Iteration {i}/{num_training_iterations_rl_alt}  BCE {rx_loss.item():.4f}', end='\\r')\n",
    "    print()  # Line break\n",
    "    \n",
    "    # Once alternating training is done, the receiver is fine-tuned\n",
    "    print('Receiver fine-tuning... ')\n",
    "    for i in range(num_training_iterations_rl_finetuning):\n",
    "        optimizer_rx.zero_grad()\n",
    "        ebno_db = torch.empty(training_batch_size, device=device).uniform_(ebno_db_min, ebno_db_max)\n",
    "        _, rx_loss = model(training_batch_size, ebno_db)\n",
    "        rx_loss.backward()\n",
    "        optimizer_rx.step()\n",
    "        if i % 100 == 0:\n",
    "            print(f'Iteration {i}/{num_training_iterations_rl_finetuning}  BCE {rx_loss.item():.4f}', end='\\r')\n",
    "    print()\n",
    "    model.eval()\n",
    "    optimizer_tx.zero_grad(set_to_none=True)\n",
    "    optimizer_rx.zero_grad(set_to_none=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdd091ad-2ec7-4375-8715-a9516b5a4a4d",
   "metadata": {},
   "source": [
    "In the next cell, an instance of the model defined previously is instantiated and trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f05bcfed-6836-4ee7-9619-9b8ea01245e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:10:50.831851Z",
     "iopub.status.busy": "2026-02-13T14:10:50.831632Z",
     "iopub.status.idle": "2026-02-13T14:12:21.452167Z",
     "shell.execute_reply": "2026-02-13T14:12:21.450978Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 6900/7000  BCE 0.3030\r\n",
      "Receiver fine-tuning... \n",
      "Iteration 0/3000  BCE 0.2799\r\n",
      "Iteration 200/3000  BCE 0.2800\r\n",
      "Iteration 400/3000  BCE 0.2843\r\n",
      "Iteration 600/3000  BCE 0.2827\r\n",
      "Iteration 800/3000  BCE 0.2819\r\n",
      "Iteration 1000/3000  BCE 0.2846\r\n",
      "Iteration 1200/3000  BCE 0.2830\r\n",
      "Iteration 1400/3000  BCE 0.2883\r\n",
      "Iteration 1600/3000  BCE 0.2873\r\n",
      "Iteration 1800/3000  BCE 0.2815\r\n",
      "Iteration 2000/3000  BCE 0.2886\r\n",
      "Iteration 2100/3000  BCE 0.2821\r\n",
      "Iteration 2300/3000  BCE 0.2895\r\n",
      "Iteration 2400/3000  BCE 0.2765\r\n",
      "Iteration 2600/3000  BCE 0.2890\r\n",
      "Iteration 2700/3000  BCE 0.2875\r\n",
      "Iteration 2900/3000  BCE 0.2878\r\n"
     ]
    }
   ],
   "source": [
    "# Instantiate and train the end-to-end system\n",
    "model = torch.compile(E2ESystemRLTraining(training=True).to(device), mode=\"reduce-overhead\")\n",
    "rl_based_training(model)\n",
    "save_weights(model, model_weights_path_rl_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd0345da-9e49-4917-8197-0787254b5cf7",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4344594-739d-485f-8ee0-fbb9c8cb9055",
   "metadata": {},
   "source": [
    "The following cell implements a baseline which uses QAM with Gray labeling and conventional demapping for AWGN channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f81f69a9-cc45-42ad-92a3-8ea469b1363e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:12:21.454457Z",
     "iopub.status.busy": "2026-02-13T14:12:21.454323Z",
     "iopub.status.idle": "2026-02-13T14:12:21.459052Z",
     "shell.execute_reply": "2026-02-13T14:12:21.458101Z"
    }
   },
   "outputs": [],
   "source": [
    "class Baseline(nn.Module):\n",
    "    \"\"\"Baseline system using QAM with Gray labeling and conventional demapping.\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        self._binary_source = BinarySource()\n",
    "        self._encoder = LDPC5GEncoder(k, n, num_bits_per_symbol)\n",
    "        constellation = Constellation(\"qam\", num_bits_per_symbol)\n",
    "        self.constellation = constellation\n",
    "        self._mapper = Mapper(constellation=constellation)\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        self._channel = AWGN()\n",
    "        \n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        self._demapper = Demapper(\"app\", constellation=constellation)\n",
    "        self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n",
    "\n",
    "    def forward(self, batch_size, ebno_db):\n",
    "        # If `ebno_db` is a scalar, a tensor with shape [batch size] is created\n",
    "        if ebno_db.dim() == 0:\n",
    "            ebno_db = ebno_db.expand(batch_size)\n",
    "        no = ebnodb2no(ebno_db, num_bits_per_symbol, coderate)\n",
    "        no = expand_to_rank(no, 2)\n",
    "        \n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        b = self._binary_source([batch_size, k])\n",
    "        c = self._encoder(b)\n",
    "        # Modulation\n",
    "        x = self._mapper(c)  # x [batch size, num_symbols_per_codeword]\n",
    "        \n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        y = self._channel(x, no)  # [batch size, num_symbols_per_codeword]\n",
    "        \n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        llr = self._demapper(y, no)\n",
    "        # Outer decoding\n",
    "        b_hat = self._decoder(llr)\n",
    "        return b, b_hat  # Ground truth and reconstructed information bits returned for BER/BLER computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9bf294cb-a410-46cf-9844-0e80ed8e1b79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:12:21.461065Z",
     "iopub.status.busy": "2026-02-13T14:12:21.460946Z",
     "iopub.status.idle": "2026-02-13T14:12:21.463594Z",
     "shell.execute_reply": "2026-02-13T14:12:21.462733Z"
    }
   },
   "outputs": [],
   "source": [
    "# Range of SNRs over which the systems are evaluated\n",
    "ebno_dbs = np.arange(ebno_db_min,  # Min SNR for evaluation\n",
    "                     ebno_db_max,  # Max SNR for evaluation\n",
    "                     0.5)  # Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6e921887-3d86-4ef1-b550-69bf086a4972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:12:21.465424Z",
     "iopub.status.busy": "2026-02-13T14:12:21.465308Z",
     "iopub.status.idle": "2026-02-13T14:12:21.468204Z",
     "shell.execute_reply": "2026-02-13T14:12:21.467359Z"
    }
   },
   "outputs": [],
   "source": [
    "# Utility function to load and set weights of a model\n",
    "def load_weights(model, model_weights_path):\n",
    "    \"\"\"Load and set weights of a model.\"\"\"\n",
    "    device = sionna.phy.config.device\n",
    "    state = torch.load(model_weights_path, map_location=device)\n",
    "    model.load_state_dict(state, strict=False)\n",
    "    # Update the constellation points from the loaded parameters\n",
    "    model.constellation.points = torch.complex(model.points_r, model.points_i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "125b4bd1-911f-4f19-b989-575c765e80d1",
   "metadata": {},
   "source": [
    "The next cell evaluate the baseline and the two autoencoder-based communication systems, trained with different method.\n",
    "The results are stored in the dictionary ``BLER``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ff0fe56c-6e7b-41f7-9b6d-a7919699ec7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:12:21.470340Z",
     "iopub.status.busy": "2026-02-13T14:12:21.470224Z",
     "iopub.status.idle": "2026-02-13T14:13:20.376008Z",
     "shell.execute_reply": "2026-02-13T14:13:20.374972Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      4.0 | 1.2266e-01 | 1.0000e+00 |       94202 |      768000 |         1024 |        1024 |        12.9 |reached target block errors\n",
      "      4.5 | 9.6753e-02 | 9.9805e-01 |       74306 |      768000 |         1022 |        1024 |         0.0 |reached target block errors\n",
      "      5.0 | 5.9906e-02 | 9.1580e-01 |       51759 |      864000 |         1055 |        1152 |         0.0 |reached target block errors\n",
      "      5.5 | 1.9831e-02 | 5.1904e-01 |       30461 |     1536000 |         1063 |        2048 |         0.0 |reached target block errors\n",
      "      6.0 | 2.6508e-03 | 1.1707e-01 |       17050 |     6432000 |         1004 |        8576 |         0.2 |reached target block errors\n",
      "      6.5 | 1.1691e-04 | 9.2785e-03 |        9450 |    80832000 |         1000 |      107776 |         2.3 |reached target block errors\n",
      "      7.0 | 5.8854e-06 | 6.1719e-04 |         565 |    96000000 |           79 |      128000 |         2.7 |reached max iterations\n",
      "      7.5 | 5.0000e-07 | 7.0312e-05 |          48 |    96000000 |            9 |      128000 |         2.7 |reached max iterations\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      4.0 | 1.0398e-01 | 9.9707e-01 |       79855 |      768000 |         1021 |        1024 |        10.1 |reached target block errors\n",
      "      4.5 | 6.5678e-02 | 9.1927e-01 |       56746 |      864000 |         1059 |        1152 |         0.0 |reached target block errors\n",
      "      5.0 | 2.3924e-02 | 5.5625e-01 |       34450 |     1440000 |         1068 |        1920 |         0.0 |reached target block errors\n",
      "      5.5 | 4.0155e-03 | 1.4976e-01 |       20431 |     5088000 |         1016 |        6784 |         0.1 |reached target block errors\n",
      "      6.0 | 2.2284e-04 | 1.4839e-02 |       11274 |    50592000 |         1001 |       67456 |         1.3 |reached target block errors\n",
      "      6.5 | 1.4771e-05 | 1.1406e-03 |        1418 |    96000000 |          146 |      128000 |         2.4 |reached max iterations\n",
      "      7.0 | 1.1458e-06 | 1.4062e-04 |         110 |    96000000 |           18 |      128000 |         2.4 |reached max iterations\n",
      "      7.5 | 4.1667e-08 | 7.8125e-06 |           4 |    96000000 |            1 |      128000 |         2.4 |reached max iterations\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      4.0 | 1.0408e-01 | 9.9414e-01 |       79931 |      768000 |         1018 |        1024 |        10.2 |reached target block errors\n",
      "      4.5 | 6.5394e-02 | 9.2188e-01 |       56500 |      864000 |         1062 |        1152 |         0.0 |reached target block errors\n",
      "      5.0 | 2.2446e-02 | 5.2135e-01 |       32322 |     1440000 |         1001 |        1920 |         0.0 |reached target block errors\n",
      "      5.5 | 3.5386e-03 | 1.3884e-01 |       19363 |     5472000 |         1013 |        7296 |         0.1 |reached target block errors\n",
      "      6.0 | 2.3212e-04 | 1.4376e-02 |       12122 |    52224000 |         1001 |       69632 |         1.3 |reached target block errors\n",
      "      6.5 | 9.7500e-06 | 9.7656e-04 |         936 |    96000000 |          125 |      128000 |         2.5 |reached max iterations\n",
      "      7.0 | 9.8958e-07 | 1.0938e-04 |          95 |    96000000 |           14 |      128000 |         2.5 |reached max iterations\n",
      "      7.5 | 2.0833e-08 | 1.5625e-05 |           2 |    96000000 |            2 |      128000 |         2.5 |reached max iterations\n"
     ]
    }
   ],
   "source": [
    "torch._dynamo.reset()\n",
    "torch.cuda.empty_cache() \n",
    "\n",
    "# Dictionary storing the results\n",
    "BLER = {}\n",
    "\n",
    "with torch.no_grad():  \n",
    "\n",
    "    model_baseline = Baseline().to(device)\n",
    "    _, bler = sim_ber(model_baseline, ebno_dbs, batch_size=128, num_target_block_errors=1000, max_mc_iter=1000, compile_mode=\"reduce-overhead\")\n",
    "    BLER['baseline'] = bler.cpu().numpy()\n",
    "\n",
    "    model_conventional = E2ESystemConventionalTraining(training=False).to(device)\n",
    "    load_weights(model_conventional, model_weights_path_conventional_training)\n",
    "    _, bler = sim_ber(model_conventional, ebno_dbs, batch_size=128, num_target_block_errors=1000, max_mc_iter=1000, compile_mode=\"reduce-overhead\")\n",
    "    BLER['autoencoder-conv'] = bler.cpu().numpy()\n",
    "\n",
    "    model_rl = E2ESystemRLTraining(training=False).to(device)\n",
    "    load_weights(model_rl, model_weights_path_rl_training)\n",
    "    _, bler = sim_ber(model_rl, ebno_dbs, batch_size=128, num_target_block_errors=1000, max_mc_iter=1000, compile_mode=\"reduce-overhead\")\n",
    "    BLER['autoencoder-rl'] = bler.cpu().numpy()\n",
    "\n",
    "with open(results_filename, 'wb') as f:\n",
    "    pickle.dump((ebno_dbs, BLER), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "905f3b1a-f906-4eab-b081-2705c9e1f6ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:13:20.378610Z",
     "iopub.status.busy": "2026-02-13T14:13:20.378476Z",
     "iopub.status.idle": "2026-02-13T14:13:20.644701Z",
     "shell.execute_reply": "2026-02-13T14:13:20.643981Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "# Baseline - Perfect CSI\n",
    "plt.semilogy(ebno_dbs, BLER['baseline'], 'o-', c='C0', label='Baseline')\n",
    "# Autoencoder - conventional training\n",
    "plt.semilogy(ebno_dbs, BLER['autoencoder-conv'], 'x-.', c='C1', label='Autoencoder - conventional training')\n",
    "# Autoencoder - RL-based training\n",
    "plt.semilogy(ebno_dbs, BLER['autoencoder-rl'], 'o-.', c='C2', label='Autoencoder - RL-based training')\n",
    "\n",
    "plt.xlabel(r\"$E_b/N_0$ (dB)\")\n",
    "plt.ylabel(\"BLER\")\n",
    "plt.grid(which=\"both\")\n",
    "plt.ylim((1e-4, 1.0))\n",
    "plt.legend()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35d47f5-226f-435d-bf65-d46cd0fad5ca",
   "metadata": {},
   "source": [
    "## Visualizing the Learned Constellations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "99fab7bb-d5fd-4173-9acc-688fb395c3a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:13:20.647388Z",
     "iopub.status.busy": "2026-02-13T14:13:20.647250Z",
     "iopub.status.idle": "2026-02-13T14:13:20.902420Z",
     "shell.execute_reply": "2026-02-13T14:13:20.901609Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_conventional = E2ESystemConventionalTraining(training=True).to(device)\n",
    "load_weights(model_conventional, model_weights_path_conventional_training)\n",
    "model_conventional.constellation.points = torch.complex(model_conventional.points_r, model_conventional.points_i)\n",
    "fig = model_conventional.constellation.show()\n",
    "fig.suptitle('Conventional training');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "772edc3d-3eb5-4219-a8d2-2853f463f84e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-13T14:13:20.904897Z",
     "iopub.status.busy": "2026-02-13T14:13:20.904747Z",
     "iopub.status.idle": "2026-02-13T14:13:21.147076Z",
     "shell.execute_reply": "2026-02-13T14:13:21.146131Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_rl = E2ESystemRLTraining(training=False).to(device)\n",
    "load_weights(model_rl, model_weights_path_rl_training)\n",
    "model_rl.constellation.points = torch.complex(model_rl.points_r, model_rl.points_i)\n",
    "fig = model_rl.constellation.show()\n",
    "fig.suptitle('RL-based training');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2774433f-05e1-4421-9e9f-a24dd2f3fd64",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e67ab4d0-2aa8-453d-a810-3c5f105567dd",
   "metadata": {},
   "source": [
    "[1] T. O’Shea and J. Hoydis, \"An Introduction to Deep Learning for the Physical Layer,\" in IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563-575, Dec. 2017, doi: 10.1109/TCCN.2017.2758370.\n",
    "\n",
    "[2] S. Cammerer, F. Ait Aoudia, S. Dörner, M. Stark, J. Hoydis and S. ten Brink, \"Trainable Communication Systems: Concepts and Prototype,\" in IEEE Transactions on Communications, vol. 68, no. 9, pp. 5489-5503, Sept. 2020, doi: 10.1109/TCOMM.2020.3002915.\n",
    "\n",
    "[3] F. Ait Aoudia and J. Hoydis, \"Model-Free Training of End-to-End Communication Systems,\" in IEEE Journal on Selected Areas in Communications, vol. 37, no. 11, pp. 2503-2516, Nov. 2019, doi: 10.1109/JSAC.2019.2933891."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.12"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}