GNN_decoder_LDPC_5G.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "8aa94ca2",
   "metadata": {},
   "source": [
    "# SPDX-FileCopyrightText: Copyright (c) 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\n",
    "# SPDX-License-Identifier: LicenseRef-NvidiaProprietary\n",
    "#\n",
    "# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual\n",
    "# property and proprietary rights in and to this material, related\n",
    "# documentation and any modifications thereto. Any use, reproduction,\n",
    "# disclosure or distribution of this material and related documentation\n",
    "# without an express license agreement from NVIDIA CORPORATION or\n",
    "# its affiliates is strictly prohibited."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "386f0ba4",
   "metadata": {},
   "source": [
    "# GNN Decoder Experiments for 5G NR LDPC codes\n",
    "\n",
    "This notebook reproduces the 5G NR LDPC code results in the paper [Graph Neural Networks for Channel Decoding](https://arxiv.org/pdf/2207.14742.pdf).\n",
    "\n",
    "**Remark**: the training can take several hours. However, pre-trained weights are stored in this repository and can be directly loaded to reproduce the results.\n",
    "\n",
    "This notebook requires [Sionna](https://nvlabs.github.io/sionna/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a65e588",
   "metadata": {},
   "outputs": [],
   "source": [
    "# general imports\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# load required Sionna components\n",
    "from sionna.fec.utils import load_parity_check_examples, LinearEncoder, gm2pcm\n",
    "from sionna.utils.plotting import PlotBER\n",
    "from sionna.fec.ldpc import LDPCBPDecoder, LDPC5GEncoder, LDPC5GDecoder\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from gnn import * # load GNN functions\n",
    "from wbp import * # load weighted BP functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bfcba1be-cac2-4316-803b-d7715674d5bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of GPUs available : 2\n",
      "Only GPU number 1 used.\n"
     ]
    }
   ],
   "source": [
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "print('Number of GPUs available :', len(gpus))\n",
    "if gpus:\n",
    "    gpu_num = 1 # Number of the GPU to be used\n",
    "    try:\n",
    "        #tf.config.set_visible_devices([], 'GPU')\n",
    "        tf.config.set_visible_devices(gpus[gpu_num], 'GPU')\n",
    "        print('Only GPU number', gpu_num, 'used.')\n",
    "        tf.config.experimental.set_memory_growth(gpus[gpu_num], True)\n",
    "    except RuntimeError as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d293650",
   "metadata": {},
   "source": [
    "## Define Hyperparameters and Load Code\n",
    "\n",
    "We define all parameters as dictionary to support different architectures for different codes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "89b34d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#----- LDPC 5G -----\n",
    "params={\n",
    "    # --- Code Parameters ---\n",
    "        \"code\": \"5G-LDPC\",\n",
    "        \"n\": 140,\n",
    "        \"k\": 60,\n",
    "    # --- GNN Architecture ----\n",
    "        \"num_embed_dims\": 16,\n",
    "        \"num_msg_dims\": 16,\n",
    "        \"num_hidden_units\": 48,\n",
    "        \"num_mlp_layers\": 3,\n",
    "        \"num_iter\": 10,\n",
    "        \"reduce_op\": \"sum\",\n",
    "        \"activation\": \"relu\",\n",
    "        \"clip_llr_to\": 20,\n",
    "        \"use_attributes\": False,\n",
    "        \"node_attribute_dims\": 0,\n",
    "        \"msg_attribute_dims\": 0,\n",
    "        \"return_infobits\": False,\n",
    "        \"use_bias\": True,        \n",
    "    # --- Training ---- # \n",
    "        \"batch_size\": [128, 128, 128], # bs, iter, lr must have same dim\n",
    "        \"train_iter\": [35000, 300000, 300000],\n",
    "        \"learning_rate\": [5e-4, 1e-4, 1e-5],\n",
    "        \"ebno_db_train\": [2, 8.],\n",
    "        \"ebno_db_eval\": 2.,       \n",
    "        \"batch_size_eval\": 1000, # batch size only used for evaluation during training\n",
    "        \"eval_train_steps\": 1000, # evaluate model every N iters\n",
    "    # --- Log ----\n",
    "        \"save_weights_iter\": 10000, # save weights every X iters\n",
    "        \"run_name\": \"LDPC_5G_01\", # name of the stored weights/logs\n",
    "        \"save_dir\": \"results/\", # folder to store results\n",
    "    # --- MC Simulation parameters ----\n",
    "        \"eval_num_iter\": 10, # number of decoding iters to evaluate\n",
    "        \"mc_iters\": 100,\n",
    "        \"mc_batch_size\": 1000,\n",
    "        \"num_target_block_errors\": 500,\n",
    "        \"ebno_db_min\": 0.,\n",
    "        \"ebno_db_max\": 4.5,\n",
    "        \"ebno_db_stepsize\": 0.5,\n",
    "        \"eval_ns\": [140, 280, 420, 280, 280, 280], # evaluate different lengths\n",
    "        \"eval_ks\": [60, 120, 180, 120, 90, 150],    \n",
    "        \"sim_esno\": False, # simulate results in EsN0\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eee184a0",
   "metadata": {},
   "source": [
    "## Generate the 5G Decoding Graph\n",
    "\n",
    "The 5G NR LDPC code is closely connected to rate-matching and, thus, a few graph pre-processing steps are required for our decoder.\n",
    "We perform the following steps:\n",
    "- Train including the first 2*Z punctured information bits (i.e., no puncturing for the training)\n",
    "- Prune the parity-check matrix as much as possible and remove shortened positions from the graph\n",
    "\n",
    "We would like to emphasize that the resulting trained GNN decoder is compliant with the standard code structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d8c3ddcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading 5G NR LDPC code\n",
      "using bg:  bg2\n",
      "# information bits: 60\n",
      "CW length after rate-matching: 140\n",
      "CW length without rm (incl. first 2*Z info bits): 160\n",
      "# punctured bits: 320\n",
      "# pruned nodes: 320\n",
      "# parity bits 100\n",
      "# shortened bits 40\n",
      "pruned pcm dimension: (100, 160)\n"
     ]
    }
   ],
   "source": [
    "# all codes must provide an encoder-layer and a pcm\n",
    "if params[\"code\"]==\"5G-LDPC\":    \n",
    "    print(\"Loading 5G NR LDPC code\") \n",
    "\n",
    "    k = params[\"k\"]\n",
    "    n = params[\"n\"]\n",
    "    \n",
    "    encoder_5g = LDPC5GEncoder(k, n)\n",
    "    decoder_5g = LDPC5GDecoder(encoder_5g,\n",
    "                               num_iter=params[\"eval_num_iter\"],\n",
    "                               return_infobits=False,\n",
    "                               prune_pcm=True)\n",
    "    \n",
    "    pcm,_ = generate_pruned_pcm_5g(decoder_5g, n)\n",
    "    \n",
    "    n_no_rm = pcm.shape[1]\n",
    "    k_no_rm = pcm.shape[1] - pcm.shape[0]\n",
    "\n",
    "    # create encoder without rate-matching\n",
    "    u_ref = np.eye(k)\n",
    "    c_ref = encoder_5g(u_ref).numpy()\n",
    "    gm = np.concatenate([u_ref[:,:2*encoder_5g._z], c_ref], axis=1)\n",
    "    encoder_no_rm = LinearEncoder(gm, is_pcm=False)\n",
    "\n",
    "else:\n",
    "    raise ValueError(\"Unknown code type\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ada6da9",
   "metadata": {},
   "source": [
    "## Simulate Baseline BER Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "343e2676-5791-45da-b7f8-812304dc79e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ber_plot = PlotBER(f\"GNN-based Decoding - {params['code']}, (k,n)=({k},{n})\")\n",
    "ebno_dbs = np.arange(params[\"ebno_db_min\"],\n",
    "                     params[\"ebno_db_max\"]+1,\n",
    "                     params[\"ebno_db_stepsize\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d360c58-668a-4eff-a6f9-acaeef510fc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 7.9320e-02 | 1.0000e+00 |        7932 |      100000 |         1000 |        1000 |         1.7 |reached target block errors\n",
      "      0.5 | 6.6310e-02 | 9.9800e-01 |        6631 |      100000 |          998 |        1000 |         0.0 |reached target block errors\n",
      "      1.0 | 5.6670e-02 | 9.9900e-01 |        5667 |      100000 |          999 |        1000 |         0.0 |reached target block errors\n",
      "      1.5 | 4.5470e-02 | 9.9100e-01 |        4547 |      100000 |          991 |        1000 |         0.0 |reached target block errors\n",
      "      2.0 | 3.8560e-02 | 9.8400e-01 |        3856 |      100000 |          984 |        1000 |         0.0 |reached target block errors\n",
      "      2.5 | 3.0290e-02 | 9.5600e-01 |        3029 |      100000 |          956 |        1000 |         0.0 |reached target block errors\n",
      "      3.0 | 2.3260e-02 | 8.9800e-01 |        2326 |      100000 |          898 |        1000 |         0.0 |reached target block errors\n",
      "      3.5 | 1.6760e-02 | 8.2600e-01 |        1676 |      100000 |          826 |        1000 |         0.0 |reached target block errors\n",
      "      4.0 | 1.2980e-02 | 7.3700e-01 |        1298 |      100000 |          737 |        1000 |         0.0 |reached target block errors\n",
      "      4.5 | 9.0400e-03 | 5.9900e-01 |         904 |      100000 |          599 |        1000 |         0.0 |reached target block errors\n",
      "      5.0 | 6.1600e-03 | 4.6900e-01 |        1232 |      200000 |          938 |        2000 |         0.0 |reached target block errors\n"
     ]
    }
   ],
   "source": [
    "# uncoded QPSK\n",
    "e2e_uncoded = E2EModel(None, None, k=100, n=100) # k and n are not relevant here\n",
    "ber_plot.simulate(e2e_uncoded,\n",
    "                  ebno_dbs=ebno_dbs,\n",
    "                  batch_size=params[\"mc_batch_size\"],\n",
    "                  num_target_block_errors=params[\"num_target_block_errors\"],\n",
    "                  legend=\"Uncoded\",\n",
    "                  soft_estimates=True,\n",
    "                  max_mc_iter=params[\"mc_iters\"],\n",
    "                  forward_keyboard_interrupt=False,\n",
    "                  show_fig=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e1ced8",
   "metadata": {},
   "source": [
    "### GNN-based Decoding without Rate-matching\n",
    "\n",
    "For the training, we ignore the rate-matching and train the decoder\n",
    "only for the given parity-check matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76868915-620b-4abe-a620-dfcb1d308b50",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(2) # we fix the seed to ensure stable convergence \n",
    "\n",
    "# init the GNN decoder\n",
    "gnn_decoder = GNN_BP(pcm=pcm,\n",
    "                     num_embed_dims=params[\"num_embed_dims\"],\n",
    "                     num_msg_dims=params[\"num_msg_dims\"],\n",
    "                     num_hidden_units=params[\"num_hidden_units\"],\n",
    "                     num_mlp_layers=params[\"num_mlp_layers\"],\n",
    "                     num_iter=params[\"num_iter\"],\n",
    "                     reduce_op=params[\"reduce_op\"],\n",
    "                     activation=params[\"activation\"],\n",
    "                     output_all_iter=True,\n",
    "                     clip_llr_to=params[\"clip_llr_to\"],\n",
    "                     use_attributes=params[\"use_attributes\"],\n",
    "                     node_attribute_dims=params[\"node_attribute_dims\"],\n",
    "                     msg_attribute_dims=params[\"msg_attribute_dims\"],\n",
    "                     use_bias=params[\"use_bias\"])\n",
    "                     \n",
    "e2e_gnn = E2EModel(encoder_no_rm, gnn_decoder, k_no_rm, n_no_rm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f251a37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"e2e_model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " binary_source_1 (BinarySour  multiple                 0         \n",
      " ce)                                                             \n",
      "                                                                 \n",
      " mapper_1 (Mapper)           multiple                  0         \n",
      "                                                                 \n",
      " demapper_1 (Demapper)       multiple                  0         \n",
      "                                                                 \n",
      " awgn_1 (AWGN)               multiple                  0         \n",
      "                                                                 \n",
      " gnn_bp (GNN_BP)             multiple                  18929     \n",
      "                                                                 \n",
      " linear_encoder (LinearEncod  multiple                 0         \n",
      " er)                                                             \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 18,929\n",
      "Trainable params: 18,929\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# init model and print summary\n",
    "e2e_gnn(1, 1.)\n",
    "e2e_gnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "56256283-07d4-42b6-9340-9985edb98159",
   "metadata": {},
   "outputs": [],
   "source": [
    "# and let's train the model...\n",
    "train = False # remark: training takes several hours\n",
    "if train:    \n",
    "    train_gnn(e2e_gnn, params)\n",
    "else:\n",
    "    # you can also load the precomputed weights\n",
    "    load_weights(e2e_gnn, \"weights/LDPC_5G_precomputed.npy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "959ac1d6",
   "metadata": {},
   "source": [
    "## Evaluate Final Performance\n",
    "\n",
    "We now evaluate the performance for different codeword lengths and different rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c08a030a-5d6a-44da-bd7f-570f59ac139b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.4625e-01 | 9.1300e-01 |       20475 |      140000 |          913 |        1000 |         3.0 |reached target block errors\n",
      "      0.5 | 1.1301e-01 | 7.9200e-01 |       15821 |      140000 |          792 |        1000 |         0.0 |reached target block errors\n",
      "      1.0 | 7.5893e-02 | 6.0400e-01 |       10625 |      140000 |          604 |        1000 |         0.0 |reached target block errors\n",
      "      1.5 | 4.6596e-02 | 3.9550e-01 |       13047 |      280000 |          791 |        2000 |         0.0 |reached target block errors\n",
      "      2.0 | 2.4633e-02 | 2.2100e-01 |       10346 |      420000 |          663 |        3000 |         0.0 |reached target block errors\n",
      "      2.5 | 1.0796e-02 | 1.0083e-01 |        9069 |      840000 |          605 |        6000 |         0.0 |reached target block errors\n",
      "      3.0 | 4.4661e-03 | 4.4083e-02 |        7503 |     1680000 |          529 |       12000 |         0.1 |reached target block errors\n",
      "      3.5 | 1.3375e-03 | 1.4111e-02 |        6741 |     5040000 |          508 |       36000 |         0.2 |reached target block errors\n",
      "      4.0 | 3.4879e-04 | 3.5900e-03 |        4883 |    14000000 |          359 |      100000 |         0.5 |reached max iter       \n",
      "      4.5 | 8.8857e-05 | 9.0000e-04 |        1244 |    14000000 |           90 |      100000 |         0.5 |reached max iter       \n",
      "      5.0 | 1.5500e-05 | 1.8000e-04 |         217 |    14000000 |           18 |      100000 |         0.5 |reached max iter       \n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.4324e-01 | 8.9700e-01 |       20054 |      140000 |          897 |        1000 |         7.1 |reached target block errors\n",
      "      0.5 | 1.0612e-01 | 7.6800e-01 |       14857 |      140000 |          768 |        1000 |         0.0 |reached target block errors\n",
      "      1.0 | 7.8714e-02 | 6.1500e-01 |       11020 |      140000 |          615 |        1000 |         0.0 |reached target block errors\n",
      "      1.5 | 4.3296e-02 | 3.9400e-01 |       12123 |      280000 |          788 |        2000 |         0.1 |reached target block errors\n",
      "      2.0 | 2.1883e-02 | 2.2333e-01 |        9191 |      420000 |          670 |        3000 |         0.1 |reached target block errors\n",
      "      2.5 | 9.6900e-03 | 1.0480e-01 |        6783 |      700000 |          524 |        5000 |         0.2 |reached target block errors\n",
      "      3.0 | 3.0750e-03 | 3.7143e-02 |        6027 |     1960000 |          520 |       14000 |         0.6 |reached target block errors\n",
      "      3.5 | 8.6398e-04 | 1.0830e-02 |        5685 |     6580000 |          509 |       47000 |         2.1 |reached target block errors\n",
      "      4.0 | 2.2543e-04 | 3.0800e-03 |        3156 |    14000000 |          308 |      100000 |         4.6 |reached max iter       \n",
      "      4.5 | 5.3714e-05 | 6.6000e-04 |         752 |    14000000 |           66 |      100000 |         4.6 |reached max iter       \n",
      "      5.0 | 9.5714e-06 | 1.4000e-04 |         134 |    14000000 |           14 |      100000 |         4.5 |reached max iter       \n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.5080e-01 | 9.8700e-01 |       42223 |      280000 |          987 |        1000 |         2.2 |reached target block errors\n",
      "      0.5 | 1.1091e-01 | 9.0500e-01 |       31054 |      280000 |          905 |        1000 |         0.0 |reached target block errors\n",
      "      1.0 | 7.1118e-02 | 7.2000e-01 |       19913 |      280000 |          720 |        1000 |         0.0 |reached target block errors\n",
      "      1.5 | 3.5550e-02 | 4.6650e-01 |       19908 |      560000 |          933 |        2000 |         0.0 |reached target block errors\n",
      "      2.0 | 1.3250e-02 | 2.0100e-01 |       11130 |      840000 |          603 |        3000 |         0.0 |reached target block errors\n",
      "      2.5 | 3.3762e-03 | 6.2556e-02 |        8508 |     2520000 |          563 |        9000 |         0.1 |reached target block errors\n",
      "      3.0 | 5.9683e-04 | 1.3944e-02 |        6016 |    10080000 |          502 |       36000 |         0.2 |reached target block errors\n",
      "      3.5 | 8.8500e-05 | 2.2100e-03 |        2478 |    28000000 |          221 |      100000 |         0.6 |reached max iter       \n",
      "      4.0 | 5.2500e-06 | 1.9000e-04 |         147 |    28000000 |           19 |      100000 |         0.6 |reached max iter       \n",
      "      4.5 | 3.5714e-07 | 2.0000e-05 |          10 |    28000000 |            2 |      100000 |         0.6 |reached max iter       \n",
      "      5.0 | 0.0000e+00 | 0.0000e+00 |           0 |    28000000 |            0 |      100000 |         0.6 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 5.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.4760e-01 | 9.8100e-01 |       41327 |      280000 |          981 |        1000 |         8.5 |reached target block errors\n",
      "      0.5 | 1.0942e-01 | 9.0200e-01 |       30639 |      280000 |          902 |        1000 |         0.1 |reached target block errors\n",
      "      1.0 | 7.3332e-02 | 7.3600e-01 |       20533 |      280000 |          736 |        1000 |         0.1 |reached target block errors\n",
      "      1.5 | 3.5002e-02 | 4.7800e-01 |       19601 |      560000 |          956 |        2000 |         0.2 |reached target block errors\n",
      "      2.0 | 1.3064e-02 | 2.2667e-01 |       10974 |      840000 |          680 |        3000 |         0.3 |reached target block errors\n",
      "      2.5 | 3.0379e-03 | 7.0250e-02 |        6805 |     2240000 |          562 |        8000 |         0.7 |reached target block errors\n",
      "      3.0 | 5.6786e-04 | 1.5875e-02 |        5088 |     8960000 |          508 |       32000 |         2.7 |reached target block errors\n",
      "      3.5 | 5.1714e-05 | 1.8500e-03 |        1448 |    28000000 |          185 |      100000 |         8.6 |reached max iter       \n",
      "      4.0 | 3.0000e-06 | 1.6000e-04 |          84 |    28000000 |           16 |      100000 |         8.6 |reached max iter       \n",
      "      4.5 | 0.0000e+00 | 0.0000e+00 |           0 |    28000000 |            0 |      100000 |         8.6 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 4.5 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.5286e-01 | 9.9700e-01 |       64201 |      420000 |          997 |        1000 |         2.2 |reached target block errors\n",
      "      0.5 | 1.1025e-01 | 9.6500e-01 |       46304 |      420000 |          965 |        1000 |         0.0 |reached target block errors\n",
      "      1.0 | 6.6317e-02 | 8.2100e-01 |       27853 |      420000 |          821 |        1000 |         0.0 |reached target block errors\n",
      "      1.5 | 2.6656e-02 | 4.9300e-01 |       22391 |      840000 |          986 |        2000 |         0.0 |reached target block errors\n",
      "      2.0 | 7.1198e-03 | 1.9900e-01 |        8971 |     1260000 |          597 |        3000 |         0.0 |reached target block errors\n",
      "      2.5 | 1.2823e-03 | 5.0455e-02 |        5924 |     4620000 |          555 |       11000 |         0.1 |reached target block errors\n",
      "      3.0 | 1.3150e-04 | 6.7973e-03 |        4087 |    31080000 |          503 |       74000 |         0.5 |reached target block errors\n",
      "      3.5 | 9.4524e-06 | 6.7000e-04 |         397 |    42000000 |           67 |      100000 |         0.7 |reached max iter       \n",
      "      4.0 | 5.4762e-07 | 5.0000e-05 |          23 |    42000000 |            5 |      100000 |         0.7 |reached max iter       \n",
      "      4.5 | 0.0000e+00 | 0.0000e+00 |           0 |    42000000 |            0 |      100000 |         0.7 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 4.5 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.5313e-01 | 9.9800e-01 |       64313 |      420000 |          998 |        1000 |         9.8 |reached target block errors\n",
      "      0.5 | 1.1063e-01 | 9.6300e-01 |       46464 |      420000 |          963 |        1000 |         0.1 |reached target block errors\n",
      "      1.0 | 6.7152e-02 | 8.3300e-01 |       28204 |      420000 |          833 |        1000 |         0.1 |reached target block errors\n",
      "      1.5 | 2.8862e-02 | 5.5300e-01 |       12122 |      420000 |          553 |        1000 |         0.1 |reached target block errors\n",
      "      2.0 | 7.7310e-03 | 2.2967e-01 |        9741 |     1260000 |          689 |        3000 |         0.4 |reached target block errors\n",
      "      2.5 | 1.4140e-03 | 6.2111e-02 |        5345 |     3780000 |          559 |        9000 |         1.1 |reached target block errors\n",
      "      3.0 | 1.3860e-04 | 8.9298e-03 |        3318 |    23940000 |          509 |       57000 |         7.2 |reached target block errors\n",
      "      3.5 | 7.4286e-06 | 7.1000e-04 |         312 |    42000000 |           71 |      100000 |        12.5 |reached max iter       \n",
      "      4.0 | 7.1429e-08 | 3.0000e-05 |           3 |    42000000 |            3 |      100000 |        12.5 |reached max iter       \n",
      "      4.5 | 0.0000e+00 | 0.0000e+00 |           0 |    42000000 |            0 |      100000 |        12.5 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 4.5 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.5026e-01 | 9.8100e-01 |       42073 |      280000 |          981 |        1000 |         2.2 |reached target block errors\n",
      "      0.5 | 1.1217e-01 | 9.1900e-01 |       31409 |      280000 |          919 |        1000 |         0.0 |reached target block errors\n",
      "      1.0 | 6.7493e-02 | 7.0700e-01 |       18898 |      280000 |          707 |        1000 |         0.0 |reached target block errors\n",
      "      1.5 | 3.3104e-02 | 4.5500e-01 |       18538 |      560000 |          910 |        2000 |         0.0 |reached target block errors\n",
      "      2.0 | 1.1151e-02 | 2.0233e-01 |        9367 |      840000 |          607 |        3000 |         0.0 |reached target block errors\n",
      "      2.5 | 3.2500e-03 | 5.9667e-02 |        8190 |     2520000 |          537 |        9000 |         0.1 |reached target block errors\n",
      "      3.0 | 6.0893e-04 | 1.4028e-02 |        6138 |    10080000 |          505 |       36000 |         0.2 |reached target block errors\n",
      "      3.5 | 8.8679e-05 | 2.2800e-03 |        2483 |    28000000 |          228 |      100000 |         0.6 |reached max iter       \n",
      "      4.0 | 8.5714e-06 | 2.1000e-04 |         240 |    28000000 |           21 |      100000 |         0.6 |reached max iter       \n",
      "      4.5 | 1.1429e-06 | 2.0000e-05 |          32 |    28000000 |            2 |      100000 |         0.6 |reached max iter       \n",
      "      5.0 | 0.0000e+00 | 0.0000e+00 |           0 |    28000000 |            0 |      100000 |         0.6 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 5.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.4592e-01 | 9.7500e-01 |       40859 |      280000 |          975 |        1000 |         7.8 |reached target block errors\n",
      "      0.5 | 1.1354e-01 | 8.9100e-01 |       31791 |      280000 |          891 |        1000 |         0.1 |reached target block errors\n",
      "      1.0 | 7.1682e-02 | 7.1100e-01 |       20071 |      280000 |          711 |        1000 |         0.1 |reached target block errors\n",
      "      1.5 | 3.4404e-02 | 4.7350e-01 |       19266 |      560000 |          947 |        2000 |         0.2 |reached target block errors\n",
      "      2.0 | 1.1835e-02 | 2.1500e-01 |        9941 |      840000 |          645 |        3000 |         0.3 |reached target block errors\n",
      "      2.5 | 3.1112e-03 | 6.9250e-02 |        6969 |     2240000 |          554 |        8000 |         0.7 |reached target block errors\n",
      "      3.0 | 5.3771e-04 | 1.4824e-02 |        5119 |     9520000 |          504 |       34000 |         2.9 |reached target block errors\n",
      "      3.5 | 5.6179e-05 | 1.8300e-03 |        1573 |    28000000 |          183 |      100000 |         8.6 |reached max iter       \n",
      "      4.0 | 5.3571e-06 | 1.8000e-04 |         150 |    28000000 |           18 |      100000 |         8.6 |reached max iter       \n",
      "      4.5 | 0.0000e+00 | 0.0000e+00 |           0 |    28000000 |            0 |      100000 |         8.6 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 4.5 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.3037e-01 | 9.0600e-01 |       36503 |      280000 |          906 |        1000 |         2.2 |reached target block errors\n",
      "      0.5 | 9.0657e-02 | 7.8200e-01 |       25384 |      280000 |          782 |        1000 |         0.0 |reached target block errors\n",
      "      1.0 | 5.4779e-02 | 5.5200e-01 |       15338 |      280000 |          552 |        1000 |         0.0 |reached target block errors\n",
      "      1.5 | 2.2579e-02 | 3.0150e-01 |       12644 |      560000 |          603 |        2000 |         0.0 |reached target block errors\n",
      "      2.0 | 6.5571e-03 | 1.1520e-01 |        9180 |     1400000 |          576 |        5000 |         0.0 |reached target block errors\n",
      "      2.5 | 1.9786e-03 | 3.8846e-02 |        7202 |     3640000 |          505 |       13000 |         0.1 |reached target block errors\n",
      "      3.0 | 3.4080e-04 | 8.6271e-03 |        5630 |    16520000 |          509 |       59000 |         0.4 |reached target block errors\n",
      "      3.5 | 6.8214e-05 | 1.5700e-03 |        1910 |    28000000 |          157 |      100000 |         0.6 |reached max iter       \n",
      "      4.0 | 9.6786e-06 | 2.4000e-04 |         271 |    28000000 |           24 |      100000 |         0.6 |reached max iter       \n",
      "      4.5 | 2.5357e-06 | 2.0000e-05 |          71 |    28000000 |            2 |      100000 |         0.6 |reached max iter       \n",
      "      5.0 | 0.0000e+00 | 0.0000e+00 |           0 |    28000000 |            0 |      100000 |         0.6 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 5.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.3745e-01 | 9.1500e-01 |       38487 |      280000 |          915 |        1000 |         8.6 |reached target block errors\n",
      "      0.5 | 9.0736e-02 | 7.8700e-01 |       25406 |      280000 |          787 |        1000 |         0.1 |reached target block errors\n",
      "      1.0 | 5.1450e-02 | 5.7100e-01 |       14406 |      280000 |          571 |        1000 |         0.1 |reached target block errors\n",
      "      1.5 | 2.2709e-02 | 3.4350e-01 |       12717 |      560000 |          687 |        2000 |         0.2 |reached target block errors\n",
      "      2.0 | 7.6562e-03 | 1.4550e-01 |        8575 |     1120000 |          582 |        4000 |         0.3 |reached target block errors\n",
      "      2.5 | 1.8512e-03 | 4.5833e-02 |        6220 |     3360000 |          550 |       12000 |         1.0 |reached target block errors\n",
      "      3.0 | 4.4229e-04 | 1.1500e-02 |        5449 |    12320000 |          506 |       44000 |         3.8 |reached target block errors\n",
      "      3.5 | 5.0357e-05 | 1.9600e-03 |        1410 |    28000000 |          196 |      100000 |         8.6 |reached max iter       \n",
      "      4.0 | 5.4286e-06 | 2.5000e-04 |         152 |    28000000 |           25 |      100000 |         8.6 |reached max iter       \n",
      "      4.5 | 2.3929e-06 | 1.0000e-04 |          67 |    28000000 |           10 |      100000 |         8.6 |reached max iter       \n",
      "      5.0 | 0.0000e+00 | 0.0000e+00 |           0 |    28000000 |            0 |      100000 |         8.6 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 5.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.3365e-01 | 9.9800e-01 |       37421 |      280000 |          998 |        1000 |         2.4 |reached target block errors\n",
      "      0.5 | 1.0845e-01 | 9.7600e-01 |       30366 |      280000 |          976 |        1000 |         0.0 |reached target block errors\n",
      "      1.0 | 6.9893e-02 | 8.4800e-01 |       19570 |      280000 |          848 |        1000 |         0.0 |reached target block errors\n",
      "      1.5 | 3.9368e-02 | 6.2100e-01 |       11023 |      280000 |          621 |        1000 |         0.0 |reached target block errors\n",
      "      2.0 | 1.4371e-02 | 3.0950e-01 |        8048 |      560000 |          619 |        2000 |         0.0 |reached target block errors\n",
      "      2.5 | 3.7414e-03 | 1.0260e-01 |        5238 |     1400000 |          513 |        5000 |         0.0 |reached target block errors\n",
      "      3.0 | 5.9253e-04 | 2.3091e-02 |        3650 |     6160000 |          508 |       22000 |         0.1 |reached target block errors\n",
      "      3.5 | 7.3179e-05 | 3.6500e-03 |        2049 |    28000000 |          365 |      100000 |         0.6 |reached max iter       \n",
      "      4.0 | 7.7500e-06 | 5.4000e-04 |         217 |    28000000 |           54 |      100000 |         0.6 |reached max iter       \n",
      "      4.5 | 1.3929e-06 | 7.0000e-05 |          39 |    28000000 |            7 |      100000 |         0.6 |reached max iter       \n",
      "      5.0 | 0.0000e+00 | 0.0000e+00 |           0 |    28000000 |            0 |      100000 |         0.6 |reached max iter       \n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 5.0 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      0.0 | 1.3519e-01 | 9.9800e-01 |       37853 |      280000 |          998 |        1000 |         8.5 |reached target block errors\n",
      "      0.5 | 1.0707e-01 | 9.7000e-01 |       29980 |      280000 |          970 |        1000 |         0.1 |reached target block errors\n",
      "      1.0 | 6.9318e-02 | 8.5000e-01 |       19409 |      280000 |          850 |        1000 |         0.1 |reached target block errors\n",
      "      1.5 | 3.8521e-02 | 6.3000e-01 |       10786 |      280000 |          630 |        1000 |         0.1 |reached target block errors\n",
      "      2.0 | 1.5454e-02 | 3.3700e-01 |        8654 |      560000 |          674 |        2000 |         0.2 |reached target block errors\n",
      "      2.5 | 3.4343e-03 | 1.0960e-01 |        4808 |     1400000 |          548 |        5000 |         0.4 |reached target block errors\n",
      "      3.0 | 5.7516e-04 | 2.2955e-02 |        3543 |     6160000 |          505 |       22000 |         1.8 |reached target block errors\n",
      "      3.5 | 6.8179e-05 | 3.6400e-03 |        1909 |    28000000 |          364 |      100000 |         8.0 |reached max iter       \n",
      "      4.0 | 5.2857e-06 | 3.6000e-04 |         148 |    28000000 |           36 |      100000 |         8.0 |reached max iter       \n",
      "      4.5 | 7.1429e-08 | 1.0000e-05 |           2 |    28000000 |            1 |      100000 |         8.0 |reached max iter       \n",
      "      5.0 | 1.4286e-07 | 2.0000e-05 |           4 |    28000000 |            2 |      100000 |         8.0 |reached max iter       \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx, n_eval in enumerate(params[\"eval_ns\"]):\n",
    "    k_eval = params[\"eval_ks\"][idx]\n",
    "    \n",
    "    # generate new code for each length\n",
    "    encoder_5g_eval = LDPC5GEncoder(k_eval , n_eval)\n",
    "    decoder_5g_eval = LDPC5GDecoder(encoder_5g_eval,\n",
    "                                    num_iter=params[\"eval_num_iter\"],\n",
    "                                    return_infobits=params[\"return_infobits\"])\n",
    "    e2e_bp = E2EModel(encoder_5g_eval,\n",
    "                      decoder_5g_eval,\n",
    "                      k_eval,\n",
    "                      n_eval,\n",
    "                      return_infobits=params[\"return_infobits\"],\n",
    "                      es_no=params[\"sim_esno\"] )\n",
    "\n",
    "    ber_plot.simulate(e2e_bp,\n",
    "                  ebno_dbs=ebno_dbs,\n",
    "                  batch_size=params[\"mc_batch_size\"],\n",
    "                  num_target_block_errors=params[\"num_target_block_errors\"],\n",
    "                  legend=f\"BP-{decoder_5g._num_iter.numpy()} n={n_eval}\",\n",
    "                  soft_estimates=True,\n",
    "                  max_mc_iter=params[\"mc_iters\"],\n",
    "                  forward_keyboard_interrupt=False,\n",
    "                  show_fig=False);\n",
    "    # instantiate new decoder for each number of iter (otherwise no retracing)\n",
    "    gnn_decoder_rm_eval = LDPC5GGNN(encoder_5g_eval,\n",
    "                     num_embed_dims=params[\"num_embed_dims\"],\n",
    "                     num_msg_dims=params[\"num_msg_dims\"],\n",
    "                     num_hidden_units=params[\"num_hidden_units\"],\n",
    "                     num_mlp_layers=params[\"num_mlp_layers\"],\n",
    "                     num_iter=params[\"eval_num_iter\"],\n",
    "                     reduce_op=params[\"reduce_op\"],\n",
    "                     activation=params[\"activation\"],\n",
    "                     output_all_iter=False,\n",
    "                     clip_llr_to=params[\"clip_llr_to\"],\n",
    "                     use_attributes=params[\"use_attributes\"],\n",
    "                     node_attribute_dims=params[\"node_attribute_dims\"],\n",
    "                     msg_attribute_dims=params[\"msg_attribute_dims\"],\n",
    "                     return_infobits=params[\"return_infobits\"],\n",
    "                     use_bias=params[\"use_bias\"])    \n",
    "    # generate new model   \n",
    "    model = E2EModel(encoder_5g_eval,\n",
    "                     gnn_decoder_rm_eval,\n",
    "                     k_eval,\n",
    "                     n_eval,\n",
    "                     return_infobits=params[\"return_infobits\"],\n",
    "                     es_no=params[\"sim_esno\"] )\n",
    "    model(1,1.) # init model\n",
    "    # copy weights from trained decoder\n",
    "    model._decoder.set_weights(gnn_decoder.get_weights())\n",
    "\n",
    "    # and run the BER simulations\n",
    "    ber_plot.simulate(model,\n",
    "                     ebno_dbs=ebno_dbs,\n",
    "                     batch_size=params[\"mc_batch_size\"],\n",
    "                     num_target_block_errors=params[\"num_target_block_errors\"],\n",
    "                     legend=f\"GNN-{model._decoder.num_iter} n={n_eval}\",\n",
    "                     soft_estimates=True,\n",
    "                     max_mc_iter=params[\"mc_iters\"],\n",
    "                     forward_keyboard_interrupt=False,\n",
    "                     show_fig=False);\n",
    "\n",
    "ber_plot(xlim=[0, 5], ylim=[1e-5, 0.2]) # show final figure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a2605f7-d421-4d51-b34e-6986dd916be8",
   "metadata": {},
   "source": [
    "**Remark**: This figures shows the results of Fig. 5a and Fig. 5b in the paper (Fig. 5a is plotted in the Es/N0 domain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f518bcd-e463-48c0-ab28-4b6a564f3a35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "snr, \tuncoded, \tbp_k60_n140, \tgnn_k60_n140, \tbp_k120_n280, \tgnn_k120_n280, \tbp_k180_n420, \tgnn_k180_n420, \tbp_k120_n280, \tgnn_k120_n280, \tbp_k90_n280, \tgnn_k90_n280, \tbp_k150_n280, \tgnn_k150_n280, \t\n",
      "0.000,\t7.932000E-02,\t1.462500E-01,\t1.432429E-01,\t1.507964E-01,\t1.475964E-01,\t1.528595E-01,\t1.531262E-01,\t1.502607E-01,\t1.459250E-01,\t1.303679E-01,\t1.374536E-01,\t1.336464E-01,\t1.351893E-01,\t\n",
      "0.500,\t6.631000E-02,\t1.130071E-01,\t1.061214E-01,\t1.109071E-01,\t1.094250E-01,\t1.102476E-01,\t1.106286E-01,\t1.121750E-01,\t1.135393E-01,\t9.065714E-02,\t9.073571E-02,\t1.084500E-01,\t1.070714E-01,\t\n",
      "1.000,\t5.667000E-02,\t7.589286E-02,\t7.871429E-02,\t7.111786E-02,\t7.333214E-02,\t6.631667E-02,\t6.715238E-02,\t6.749286E-02,\t7.168214E-02,\t5.477857E-02,\t5.145000E-02,\t6.989286E-02,\t6.931786E-02,\t\n",
      "1.500,\t4.547000E-02,\t4.659643E-02,\t4.329643E-02,\t3.555000E-02,\t3.500179E-02,\t2.665595E-02,\t2.886190E-02,\t3.310357E-02,\t3.440357E-02,\t2.257857E-02,\t2.270893E-02,\t3.936786E-02,\t3.852143E-02,\t\n",
      "2.000,\t3.856000E-02,\t2.463333E-02,\t2.188333E-02,\t1.325000E-02,\t1.306429E-02,\t7.119841E-03,\t7.730952E-03,\t1.115119E-02,\t1.183452E-02,\t6.557143E-03,\t7.656250E-03,\t1.437143E-02,\t1.545357E-02,\t\n",
      "2.500,\t3.029000E-02,\t1.079643E-02,\t9.690000E-03,\t3.376190E-03,\t3.037946E-03,\t1.282251E-03,\t1.414021E-03,\t3.250000E-03,\t3.111161E-03,\t1.978571E-03,\t1.851190E-03,\t3.741429E-03,\t3.434286E-03,\t\n",
      "3.000,\t2.326000E-02,\t4.466071E-03,\t3.075000E-03,\t5.968254E-04,\t5.678571E-04,\t1.314994E-04,\t1.385965E-04,\t6.089286E-04,\t5.377101E-04,\t3.407990E-04,\t4.422890E-04,\t5.925325E-04,\t5.751623E-04,\t\n",
      "3.500,\t1.676000E-02,\t1.337500E-03,\t8.639818E-04,\t8.850000E-05,\t5.171429E-05,\t9.452381E-06,\t7.428571E-06,\t8.867857E-05,\t5.617857E-05,\t6.821429E-05,\t5.035714E-05,\t7.317857E-05,\t6.817857E-05,\t\n",
      "4.000,\t1.298000E-02,\t3.487857E-04,\t2.254286E-04,\t5.250000E-06,\t3.000000E-06,\t5.476190E-07,\t7.142857E-08,\t8.571429E-06,\t5.357143E-06,\t9.678571E-06,\t5.428571E-06,\t7.750000E-06,\t5.285714E-06,\t\n",
      "4.500,\t9.040000E-03,\t8.885714E-05,\t5.371429E-05,\t3.571429E-07,\t0.000000E+00,\t0.000000E+00,\t0.000000E+00,\t1.142857E-06,\t0.000000E+00,\t2.535714E-06,\t2.392857E-06,\t1.392857E-06,\t7.142857E-08,\t\n",
      "5.000,\t6.160000E-03,\t1.550000E-05,\t9.571429E-06,\t0.000000E+00,\t0.000000E+00,\t0.000000E+00,\t0.000000E+00,\t0.000000E+00,\t0.000000E+00,\t0.000000E+00,\t0.000000E+00,\t0.000000E+00,\t1.428571E-07,\t\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# save results for pgf plots\n",
    "col_names = [\"uncoded\"]\n",
    "for idx,n in enumerate(params[\"eval_ns\"]):\n",
    "    col_names.append(\"bp_k\" + str(params[\"eval_ks\"][idx]) + \"_n\" + str(n))\n",
    "    col_names.append(\"gnn_k\" + str(params[\"eval_ks\"][idx]) + \"_n\" + str(n))\n",
    "export_pgf(ber_plot, col_names)"
   ]
  }
 ],
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