Elena-SNN_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spiking Neural Belief Propagation Decoding for Short Block Length LDPC Codes\n",
    "---\n",
    "Alexander von Bank, Eike-Manuel Edelmann, Sisi Miao, Jonathan Mandelbaum and Laurent Schmalen\n",
    "\n",
    "We approximate the sum-product decoder's update equations using Spiking Neural Networks (SNNs).\n",
    "The SNN fires if the lowest incoming likelihood value at a parity check node is below a adjustable threshold.\n",
    "\n",
    "The full paper [1] is available on arXiv and IEEE Xplore.\n",
    "\n",
    "This jupyter notebook shows an exemplary implementation and usage of the Elena-SNN algorithm. The code is implemented close to the description in [1]. The comments within the source code assume prior knowledge in the field of message passing on factor graphs and assume that you have completely studied the ideas and notations in [1]. In specific, this notebook contains:\n",
    "* Generic implementation of the Elena-SNN algorithm, parallelized to handle multiple batches of data blocks in parallel. The class is implemented with pyTorch according to Norse [2] and can run on CPUs or GPUs.\n",
    "* Effects of the parameters on the BER.\n",
    "* Some helper classes.\n",
    "\n",
    "[1] A. von Bank, E.-M. Edelmann, S. Miao, J. Mandelbaum, L. Schmalen, \"Spiking Neural Belief Propagation Decoding for Short Block Length LDPC Codes\" in IEEE Trans. Commun., vol. ??, no. ??, pp. ???-???, ???. ????.\n",
    "\n",
    "[2] C. Pehle and J. E. Pedersen, \"Norse - A deep learning library for spiking\n",
    "neural networks,\" Jan. 2021, documentation: https://norse.ai/docs/.\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 101001899). Parts of this work were carried out in the framework of the CELTIC-NEXT project AI-NET-ANTILLAS (C2019/3-3) (grant agreement 16KIS1316) and within the project Open6GHub (grant agreement 16KISK010) funded by the German Federal Ministry of Education and Research (BMBF).\n",
    "\n",
    "---\n",
    "\n",
    "Copyright (c) 2023-2024 Alexander von Bank - Communications Engineering Lab (CEL), Karlsruhe Institute of Technology (KIT)\n",
    "\n",
    "<sup> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
    "\n",
    "<sup> The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
    "\n",
    "<sup> THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import external libraries\n",
    "import numpy as np\n",
    "import torch\n",
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as func\n",
    "\n",
    "from scipy.sparse import csc_matrix as sp_mat\n",
    "\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "# showing figures inline\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotting options \n",
    "font = {'size'   : 20}\n",
    "plt.rc('font', **font)\n",
    "plt.rc('text', usetex=True)\n",
    "\n",
    "matplotlib.rc('figure', figsize=(18, 10) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_alist_file(filename):\n",
    "    # H = read_alist_file(\"<path/filename>\") <== Usage\n",
    "    try:\n",
    "        fileobject = open(filename, 'r');\n",
    "    except:\n",
    "        print(\"Error 404 File not found\");\n",
    "\n",
    "    text = fileobject.readline();\n",
    "    int_list = text.split();\n",
    "    N = int(int_list[0]); \n",
    "    M = int(int_list[1]);\n",
    "\n",
    "    text = fileobject.readline();\n",
    "    int_list = text.split();\n",
    "    max_sum_dv = int(int_list[0]);\n",
    "    max_sum_dc = int(int_list[1]);\n",
    "\n",
    "    text = fileobject.readline();\n",
    "    int_list = text.split();\n",
    "    sum_dv = np.zeros(N);\n",
    "    sum_dv = np.array(int_list,dtype='int');\n",
    "\n",
    "    text = fileobject.readline();\n",
    "    int_list = text.split();\n",
    "    sum_dc = np.zeros(M);\n",
    "    sum_dc = np.array(int_list,dtype='int');\n",
    "\n",
    "    one_pos_row_wise = np.zeros((N,max_sum_dv));\n",
    "    for n in range(N):\n",
    "        text = fileobject.readline();\n",
    "        int_list = text.split();\n",
    "        #print(\"Dim of input: \"+str(len(int_list)))\n",
    "        one_pos_row_wise[n][:] = np.array(int_list,dtype='int');\n",
    "\n",
    "    one_pos_col_wise = np.zeros((M,max_sum_dc));\n",
    "    for m in range(M):\n",
    "        text = fileobject.readline();\n",
    "        int_list = text.split();\n",
    "        one_pos_col_wise[m][:] = np.array(int_list,dtype='int');\n",
    "\n",
    "    fileobject.close();\n",
    "\n",
    "    idx_row = np.nonzero(one_pos_row_wise > 0);\n",
    "    one_cnt = np.size(idx_row[0]);\n",
    "\n",
    "    row_i = np.zeros(one_cnt);\n",
    "    col_i = np.zeros(one_cnt);\n",
    "    for n in range(one_cnt):\n",
    "        row_i[n] = one_pos_row_wise[int(idx_row[0][n])][int(idx_row[1][n])];\n",
    "        col_i[n] = idx_row[0][n];\n",
    "\n",
    "    H = sp_mat( (np.ones(one_cnt), (row_i-1,col_i)) , shape=(M,N));\n",
    "\n",
    "    return H;\n",
    "\n",
    "########################\n",
    "\n",
    "sigmoid = nn.Sigmoid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spiking Neural Networks\n",
    "---\n",
    "Below we define the dynamics of a spiking neurons, a simple leaky integrate neuron (LI) and a leaky integrate-and-fire neuron (LIF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def LI(x,v,i,tau_mem_inv, tau_syn_inv,dt=1e-3):\n",
    "    i_jump = i + x\n",
    "    # compute voltage updates\n",
    "    dv = dt * tau_mem_inv * ((0 - v) + i_jump)\n",
    "    v = v + dv\n",
    "\n",
    "    # compute current updates\n",
    "    di = -dt * tau_syn_inv * i_jump\n",
    "    i = i_jump + di\n",
    "\n",
    "    return v,i\n",
    "\n",
    "def heaviside(data):\n",
    "    r\"\"\"\n",
    "    From Norse\n",
    "    \"\"\"\n",
    "    return torch.gt(data, torch.as_tensor(0.0).to(data.device)).to(data.device).to(data.dtype)  # pragma: no cover\n",
    "\n",
    "def LIF(input_spikes,v,i,tau_mem_inv,tau_syn_inv,dt=1e-3):\n",
    "    # compute current jumps\n",
    "    i_jump = i + input_spikes\n",
    "    # compute voltage updates\n",
    "    dv = dt * tau_mem_inv * ((0 - v) + i_jump)\n",
    "    v_decayed = v + dv\n",
    "\n",
    "    # compute current updates\n",
    "    di = -dt * tau_syn_inv * i_jump\n",
    "    i_decayed = i_jump + di\n",
    "\n",
    "    # compute new spikes\n",
    "    z_new = heaviside(v_decayed - 1.0)\n",
    "    # compute reset\n",
    "    v_new = (1 - z_new.detach()) * v_decayed\n",
    "\n",
    "    return z_new, v_new, i_decayed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following, incoming spikes of different amplitudes are applied to the LIF neuron.\n",
    "\n",
    "Test different values for tau_mem and tau_syn to get a feeling how spiking neurons behave."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Synaptic current')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_spikes    = torch.zeros((20,1),device=device)\n",
    "\n",
    "#Define input spikes and their amplitude here\n",
    "input_spikes[0] = 2\n",
    "input_spikes[1] = 1\n",
    "input_spikes[2] = 1\n",
    "input_spikes[3] = 1\n",
    "\n",
    "output_spikes    = torch.zeros((20,1),device=device)\n",
    "v               = torch.zeros((21,1),device=device)\n",
    "i               = torch.zeros((21,1),device=device)\n",
    "\n",
    "#Adjust the membrane and synaptic time constant here\n",
    "tau_mem = 10e-3\n",
    "tau_syn = 5e-3\n",
    "#tau_mem = 1e-3\n",
    "#tau_syn = 1e-3\n",
    "\n",
    "for ts in range(output_spikes.shape[0]):\n",
    "    output_spikes[ts], v[ts+1], i[ts+1] = LIF(input_spikes[ts],v[ts],i[ts],1/tau_mem,1/tau_syn,dt=1e-3)\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.subplot(221)\n",
    "plt.stem(input_spikes.detach().cpu().numpy())\n",
    "plt.title(\"Input Spikes\")\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.stem(output_spikes.detach().cpu().numpy())\n",
    "plt.title(\"Output Spikes\")\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.plot(v.detach().cpu().numpy())\n",
    "plt.title(\"Membrane potential\")\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.plot(i.detach().cpu().numpy())\n",
    "plt.title(\"Synaptic current\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################################################\n",
    "# This function creates the indices corresponding to the\n",
    "# variable and check nodes, with the indices a sparse matrix\n",
    "# multiplication of binary matrices is implemented by slicing \n",
    "def make_SNN(H):\n",
    "    #Transform Parity Check Matrix to integer\n",
    "    H = H.astype(int)\n",
    "    #Calculate maximum check node degree\n",
    "    d_c_max = np.max(np.sum(H,axis=1))\n",
    "\n",
    "    #Create index matrix, each nonzero element in H gets ascending number\n",
    "    idx = np.zeros_like(H)\n",
    "    idx[np.nonzero(H)] = np.cumsum(H[np.nonzero(H)])\n",
    "    #For regular LDPC codes d_c_max is constant, thus create matrix for checknode input indices\n",
    "    cn_idx = np.zeros((np.sum(H),d_c_max-1))\n",
    "    #Loop over SPC\n",
    "    for n in range(H.shape[0]):\n",
    "        #Each row of H creates d_c_max-1 checks\n",
    "        for k in range(d_c_max):\n",
    "            #Input all indices except the k-th one (extrinsic message)\n",
    "            cn_idx[n*d_c_max+k] = idx[n][np.delete(np.nonzero(H[n])[0],k)]\n",
    "    #Convert counting from 1,...,N to 0,...,N-1 for indexing\n",
    "    cn_idx -= 1\n",
    "\n",
    "    #Create 2D lists, inputs to variable nodes from check nodes and input to variable nodes from channel input values\n",
    "    vn_idx = [[]]\n",
    "    llr_idx = [[]]\n",
    "    for n in range(np.sum(H)-1):\n",
    "        vn_idx.append([])\n",
    "        llr_idx.append([])\n",
    "\n",
    "    #Create 1D list for variable node messages to output\n",
    "    out_idx = []\n",
    "\n",
    "    #Transpose indices\n",
    "    idx = idx.T\n",
    "    #Loop over colums\n",
    "    for n in range(H.shape[1]):\n",
    "        nz = np.nonzero(idx[n])[0]\n",
    "        #All nonzero elements in column define \"n-th\" output bit\n",
    "        out_idx.append((idx[n][nz]-1).tolist())\n",
    "        for k in range(len(nz)):\n",
    "            #Exclude \"k-th\" message (extrinsic message)\n",
    "            vn_idx[(idx[n][nz[k]]-1)] = (idx[n][np.delete(nz,k)]-1).tolist()\n",
    "            #All nonzero elements in column need \"n-th\" channel input value\n",
    "            llr_idx[(idx[n][nz[k]]-1)].append(n)\n",
    "\n",
    "    return cn_idx, vn_idx, llr_idx, out_idx\n",
    "\n",
    "class SNN_Decoder(nn.Module):\n",
    "    def __init__(self, N, H, device, dt=0.001):\n",
    "        super(SNN_Decoder, self).__init__()\n",
    "        self.device = device;\n",
    "\n",
    "        self.N               =  N\n",
    "\n",
    "        self.H               = H\n",
    "\n",
    "        self.dt              = dt\n",
    "\n",
    "        self.d_c_max     = np.max(np.sum(self.H,axis=1)).astype(int)\n",
    "        self.No_messages = np.sum(self.H).astype(int) \n",
    "        \n",
    "        #############################################################################################################################################\n",
    "        # SNN Weights and Biases                                                                                                                    #\n",
    "        #############################################################################################################################################\n",
    "\n",
    "        #Indices of the messages beloning to the corresponding SPC unit\n",
    "        cn_idx, vn_idx, llr_idx, out_idx = make_SNN(H)\n",
    "\n",
    "        self.checknode_indices      = cn_idx\n",
    "        self.varnode_input_messages = vn_idx\n",
    "        self.varnode_input_LLR      = llr_idx\n",
    "        self.varnode_to_LLR         = out_idx\n",
    "\n",
    "        ####################################################################################################################\n",
    "        # Weights dimensions:                                                                                              #\n",
    "        # (Number of '1' in the Parity Check Matrix, Batch Size, Output Size of Linear Layer, Input Size of Linear Layer)  #\n",
    "        # Matmul casts the Linear Layers to the Inputs dimensions                                                          #\n",
    "        ####################################################################################################################\n",
    "        \n",
    "        #Hidden layer to combine the LIF neuron outputs\n",
    "        self.fc_0   = nn.Parameter(torch.zeros((1,1,1,(self.d_c_max-1)),device=device).float(),\n",
    "                                   requires_grad=False)\n",
    "        self.fc_0[0,0,0,::] =  1\n",
    "\n",
    "\n",
    "        #Input layer of the SNN multiplier of ternary values (-1,0,1)\n",
    "        self.fcmul0   = nn.Parameter(torch.zeros((1,1,2,2),device=device).float(),\n",
    "                                   requires_grad=False)\n",
    "        self.fcmul0.data[0,0,0,0] =  1\n",
    "        self.fcmul0.data[0,0,0,1] =  1\n",
    "        self.fcmul0.data[0,0,1,0] =  1\n",
    "        self.fcmul0.data[0,0,1,1] = -1\n",
    "\n",
    "        #Output layer of the SNN multiplier of ternary values (-1,0,1)\n",
    "        self.fcmul1   = nn.Parameter(torch.zeros((1,1,1,2),device=device).float(),\n",
    "                                   requires_grad=False)\n",
    "        self.fcmul1.data[0,0,0,0] =  1\n",
    "        self.fcmul1.data[0,0,0,1] = -1\n",
    "\n",
    "        #LI Neuron time constants\n",
    "        self.tau_mem = 0.001\n",
    "        self.tau_syn = 0.001\n",
    "\n",
    "        #Bias (Theta 1)\n",
    "        self.th_bias = 2\n",
    "\n",
    "        #Amplitude of the incrementation (Theta 2)\n",
    "        self.amp = 1\n",
    "\n",
    "    def forward(self, x):\n",
    "        #Define how may iterations the BP will run\n",
    "        seq_length=20\n",
    "\n",
    "        #Get the batch size\n",
    "        batch_size,_ = x.shape\n",
    "\n",
    "        #Output value buffer\n",
    "        out           = torch.zeros(x.shape[0], self.N, device=self.device)\n",
    "        #Number of hidden spikes get accumulated here\n",
    "        hidden_spikes = 0\n",
    "\n",
    "        #Buffer for feed back of the Checknode Messages to the Variable Nodes\n",
    "        z_rec = torch.zeros( (x.shape[0],self.No_messages), device=self.device)    \n",
    "\n",
    "        #States of the neurons\n",
    "        v0 = 0\n",
    "        i0 = 0\n",
    "        v1 = 0\n",
    "        i1 = 0\n",
    "        v2 = 0\n",
    "        i2 = 0\n",
    "        v3 = 0\n",
    "        i3 = 0\n",
    "\n",
    "        #Iterate BP\n",
    "        for ts in range(seq_length):\n",
    "            # Output: (Batchsize , No. Messages)\n",
    "            # Input LLR to corresponding \"1\" entries in parity check matrix\n",
    "            z = x[::,self.varnode_input_LLR].flatten(-2)\n",
    "            \n",
    "            # Checknode messages to corresponding varnodes (Iteration over No_Messages if VN degree not even)\n",
    "            z += torch.sum(z_rec[::,self.varnode_input_messages],axis=2)\n",
    "\n",
    "            # Varnode messages to corresponding varnodes (output) (Iteration over No_Messages if VN degree not even)\n",
    "            out = torch.sum(z[::,self.varnode_to_LLR],axis=2)\n",
    "\n",
    "            # Select corresponding VN messages for CN update Output: (Batchsize, No. Messages, dv - 1)\n",
    "            z = z[::,self.checknode_indices]\n",
    "\n",
    "            ############################################################################\n",
    "            #                            Elena-SNN* part                               #\n",
    "            ############################################################################\n",
    "            signs = torch.prod(torch.sign(z),axis=2,keepdim=True)            \n",
    "\n",
    "            ############################################################################\n",
    "            # Extended part which deactivates the incrementation of the likelihoods    #\n",
    "            ############################################################################\n",
    "\n",
    "            ############################################################################\n",
    "            # Approximation of the Comparator with LIF neurons\n",
    "\n",
    "            #If a value is below bias (Theta_1), a positive value occurs at neuron input and charges Neuron\n",
    "            #High weight and low time constants ensure the neuron directly fires a pulse\n",
    "            z    = -10*(torch.abs(z)-self.th_bias)\n",
    "\n",
    "            #LIF neuron with low time constants, input is inverted tau mem, so time constants are 1ms\n",
    "            z,v0,i0 = LIF(z,v0,i0,1000,1000,dt=self.dt);\n",
    "            #Keep count of emitted spikes to calculate efficiency later on\n",
    "            hidden_spikes += torch.sum(z)\n",
    "            #\n",
    "            #############################################################################\n",
    "\n",
    "            ############################################################################\n",
    "            # Approximation of the AND with LIF neurons\n",
    "            #\n",
    "            #Combine spikes of all dc - 1 LIF neurons, amplify the result and send them to neurons with\n",
    "            #small time constants to approximate an OR element\n",
    "            #The combination of Inverted Threshold and OR acts as Threshold with AND but with fewer spikes\n",
    "            z    = 10*torch.matmul(self.fc_0,z.reshape((z.shape[0],z.shape[1],z.shape[2],1))).flatten(-2)\n",
    "            z,v1,i1 = LIF(z,v1,i1,1000,1000,dt=self.dt);\n",
    "            #Keep count of emitted spikes to calculate efficiency later on\n",
    "            hidden_spikes += torch.sum(z)\n",
    "\n",
    "            # Invert message, if one LIF neuron spikes, the output is 0\n",
    "            # Otherwise it's 1\n",
    "            z = 1-z\n",
    "            #\n",
    "            #############################################################################\n",
    "\n",
    "            ############################################################################\n",
    "            # Approximation of the multiplication in front of the LI neuron with LIF neurons\n",
    "            z       = torch.cat((z,signs),axis=2)\n",
    "            z       = torch.matmul(self.fcmul0,z.reshape((z.shape[0],z.shape[1],z.shape[2],1))).flatten(-2)\n",
    "            z       = 0.501/1*z\n",
    "            z,v2,i2 = LIF(z,v2,i2,1000,1000,dt=self.dt);\n",
    "            z       = torch.matmul(self.fcmul1,z.reshape((z.shape[0],z.shape[1],z.shape[2],1))).flatten(-2)\n",
    "            z    = z.flatten(-2)\n",
    "\n",
    "            #Scale with amplitude (Theta_2) at this point, since this LIF approximation of mult. can only handle ternary values\n",
    "            z = self.amp * z\n",
    "            #\n",
    "            #############################################################################\n",
    "            \n",
    "            #LI after multiplication (Relevant for Elena-SNN*, minor relevance for Elena-SNN)\n",
    "            v3,i3 = LI(z,v3,i3,1/self.tau_mem, 1/self.tau_syn,dt=self.dt);\n",
    "            z_rec = v3.clone()\n",
    "\n",
    "        del z\n",
    "\n",
    "        #Spikerate = Number of Spikes emitted / (Number of parallel computed Codewords * No of Ones in H * Timesteps * No of LIF Neurons)\n",
    "        spikerate = hidden_spikes/(x.shape[0]*self.No_messages*seq_length*(self.d_c_max))\n",
    "\n",
    "        return out, spikerate\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SNN_Decoder()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get current path\n",
    "notebook_path = os.path.abspath(\"Notebook.ipynb\")\n",
    "Decode_alist = os.path.join(os.path.dirname(notebook_path), \"FG_273-191.alist\")\n",
    "\n",
    "#Read .alist file to get Parity Check matrix\n",
    "H = read_alist_file(Decode_alist).toarray();\n",
    "\n",
    "N = H.shape[1]\n",
    "\n",
    "#Create Elena-SNN\n",
    "decoder = SNN_Decoder(N, H, device, dt=0.001)\n",
    "decoder.to(device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EBN0: 0.0\n",
      "EBN0: 0.5\n",
      "EBN0: 1.0\n",
      "EBN0: 1.5\n",
      "EBN0: 2.0\n",
      "EBN0: 2.5\n",
      "EBN0: 3.0\n",
      "EBN0: 3.5\n"
     ]
    }
   ],
   "source": [
    "#Define Eb/N0 values to simulate \n",
    "EBN0_dB = np.arange(0,4.0,0.5)\n",
    "\n",
    "#Channel reliability Es/N0 that is used throughout the simulation\n",
    "op_point = 3.5\n",
    "\n",
    "#Epochs\n",
    "EPOCHS = 5\n",
    "\n",
    "#Number of codewords\n",
    "CW = 200\n",
    "\n",
    "#Keep this fixed since we use a (273,191) FG LDPC code\n",
    "N = 273\n",
    "K = 191\n",
    "rate = K/N\n",
    "\n",
    "ERRORS   = np.zeros(EBN0_dB.shape)\n",
    "F_ERRORS = np.zeros(EBN0_dB.shape)\n",
    "BER      = np.zeros(EBN0_dB.shape)\n",
    "FER      = np.zeros(EBN0_dB.shape)\n",
    "\n",
    "for idx_P, snr_i in enumerate(EBN0_dB):\n",
    "    print(\"EBN0: \"+str(snr_i))\n",
    "    for epoch_cnt in range(EPOCHS):\n",
    "        bits = torch.zeros((CW,N),device=device);                                    #Zero codeword\n",
    "        x      = -2*(bits-0.5)          # Cast 1 to -1 and 0 to 1                    #BPSK\n",
    "\n",
    "        esn0_dB = snr_i + 10 * np.log10( rate )                                      #Convert Eb/N0 to Es/N0\n",
    "        sigma = np.sqrt( 0.5*(10**(-esn0_dB / 10)) );                                #Calculate sigma\n",
    "        noise = torch.randn(x.shape,device=device);                                  #Create noise\n",
    "        noise = sigma * noise;                                                       #Scale noise to correct SNR\n",
    "        y = x + noise                                                                #Apply noise\n",
    "        \n",
    "        y = 4*10**(op_point/10) * y                                                  # Channel output to LLR\n",
    "\n",
    "        outputs, reg_2 = decoder(y)                                                  #Apply Elena-SNN\n",
    "\n",
    "        recv_bits = torch.where(sigmoid(-1*outputs)>=0.5,1,0)                        #Hard decision\n",
    "        ERRORS[idx_P] += torch.sum(torch.abs(recv_bits-bits)).detach().cpu().numpy() #Calculate bit errors\n",
    "        F_ERRORS[idx_P] += torch.sum(torch.where(torch.sum(torch.abs(recv_bits-bits),axis=1)>0,1,0)).detach().cpu().numpy() #Calculate frame errors\n",
    "        \n",
    "BER = ERRORS / (N*CW*EPOCHS)                                                         #Calculate BER\n",
    "FER = F_ERRORS / (CW*EPOCHS)                                                         #Calculate FER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x79e07e2b8530>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Paper_EBN0_dB = np.arange(0,5.1,0.1)\n",
    "Paper_BER = np.array( [ 1.24844293e-01,1.21950681e-01,1.18588063e-01,1.15107749e-01,\n",
    "                    1.11980314e-01,1.08146283e-01,1.04380209e-01,1.00177696e-01,\n",
    "                    9.60078534e-02,9.14014660e-02,8.59165445e-02,8.05009424e-02,\n",
    "                    7.48015707e-02,6.81202094e-02,6.20061780e-02,5.57209424e-02,\n",
    "                    4.91596859e-02,4.27315183e-02,3.62655497e-02,3.02879581e-02,\n",
    "                    2.45518325e-02,1.99482723e-02,1.53648168e-02,\n",
    "                    1.17875445e-02, \n",
    "                    8.77079058e-03, 6.30443979e-03, 4.46521990e-03, 3.05641361e-03, \n",
    "                    2.03126702e-03, 1.31959686e-03, 8.19230366e-04, 5.03057592e-04,\n",
    "                    2.94204188e-04, 1.63937173e-04, 9.67382199e-05, 5.14240838e-05, \n",
    "                    2.64921466e-05, 1.41722513e-05, 7.03560209e-06, 3.43664921e-06,\n",
    "                    1.85863874e-06, 8.16230366e-07, 4.48167539e-07, 2.08376963e-07,\n",
    "                    5.92161172e-08, 3.01318681e-08, 1.18095238e-08, 7.17948718e-09,\n",
    "                    2.47619048e-09, 4.10256410e-10, 5.86080586e-10] )\n",
    "\n",
    "Paper_FER = np.array(   [9.9620e-01, 9.9470e-01, 9.9198e-01, 9.8784e-01, 9.8160e-01, 9.7230e-01,\n",
    "                         9.6290e-01, 9.4808e-01, 9.2950e-01, 9.0666e-01, 8.7592e-01, 8.4180e-01,\n",
    "                         8.0030e-01, 7.5306e-01, 6.9682e-01, 6.3614e-01, 5.7750e-01, 5.0576e-01,\n",
    "                         4.3890e-01, 3.7396e-01, 3.0800e-01, 2.4910e-01, 2.0380e-01, 1.5370e-01,\n",
    "                         1.2014e-01, 8.6120e-02, 6.2940e-02, 4.1620e-02, 2.7680e-02, 1.7500e-02,\n",
    "                         1.1472e-02, 7.0800e-03, 4.1960e-03, 2.3800e-03, 1.3420e-03, 7.2200e-04,\n",
    "                         3.5400e-04, 1.8726e-04, 9.4780e-05, 4.4100e-05, 2.2540e-05, 9.7600e-06, \n",
    "                         4.2600e-06, 2.3200e-06, \n",
    "                         9.70e-07, 5.10e-07, 2.06e-07, 1.32e-07, \n",
    "                         5.40e-08, 1.00e-08, 1.20e-08] )\n",
    "\n",
    "plt.figure()\n",
    "plt.semilogy(EBN0_dB,BER,label='Sim.')\n",
    "plt.semilogy(Paper_EBN0_dB,Paper_BER,label='Paper')\n",
    "plt.xlim(0,4.5)\n",
    "plt.ylim(5e-8,0.2)\n",
    "plt.xlabel(\"$\\\\frac{E_b}{N_0}$\")\n",
    "plt.ylabel(\"BER\")\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "plt.semilogy(EBN0_dB,FER,label='Sim.')\n",
    "plt.semilogy(Paper_EBN0_dB,Paper_FER,label='Paper')\n",
    "plt.xlim(0,4.5)\n",
    "plt.ylim(5e-8,1.0)\n",
    "plt.xlabel(\"$\\\\frac{E_b}{N_0}$\")\n",
    "plt.ylabel(\"FER\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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