DF-CF-Plot_.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b518cbf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/users/yacibena84/CodeVF/2022/GIT-2022'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from multiprocessing import Pool\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.ops import nn\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Lambda, Dropout\n",
    "from tensorflow.keras import Input, Model\n",
    "from utils import *\n",
    "from functions import * \n",
    "from DNN_metrics import * \n",
    "from loss_function import * \n",
    "from model_DL import *\n",
    "from metrics import *\n",
    "\n",
    "py_file_location = \"...\"\n",
    "os.path.abspath(os.path.join(os.path.dirname(py_file_location), os.path.pardir))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#tf.device('GPU:1') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d4c92c7",
   "metadata": {},
   "source": [
    "# Load CF data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9bd63d05",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'stats/db_gap_N_DNN_CF.npz'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_374398/3497052838.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m#-----------------------------(DNN robust training)------------------------------------#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mdb_gap_N_DNN_CF\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"db_gap_N_DNN_CF.npz\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0mmean_outage_ARPD_N_DNN_CF\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"mean_outage_ARPD_N_DNN_CF.npz\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    414\u001b[0m             \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    415\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 416\u001b[0;31m             \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menter_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    417\u001b[0m             \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'stats/db_gap_N_DNN_CF.npz'"
     ]
    }
   ],
   "source": [
    "path_1 = 'stats/'\n",
    "\n",
    "#-----------------------------(Closed form solution)------------------------------------#\n",
    "\n",
    "db_gap_CF = np.load(path_1+\"db_gap_CF.npz\")\n",
    "\n",
    "mean_outage_ARPD_CF = np.load(path_1+\"mean_outage_ARPD_CF.npz\")\n",
    "\n",
    "outage_ARPD_CF = np.load(path_1+\"outage_ARPD_CF.npz\")\n",
    "#-----------------------------(DNN perfect CSI only)------------------------------------#\n",
    "db_gap_DNN_CF = np.load(path_1+\"db_gap_DNN_CF.npz\")\n",
    "\n",
    "mean_outage_ARPD_DNN_CF = np.load(path_1+\"mean_outage_ARPD_DNN_CF.npz\")\n",
    "\n",
    "outage_ARPD_DNN_CF = np.load(path_1+\"outage_ARPD_DNN_CF.npz\")\n",
    "#-----------------------------(DNN robust training)------------------------------------#\n",
    "\n",
    "db_gap_N_DNN_CF = np.load(path_1+\"db_gap_N_DNN_CF.npz\")\n",
    "\n",
    "mean_outage_ARPD_N_DNN_CF = np.load(path_1+\"mean_outage_ARPD_N_DNN_CF.npz\")\n",
    "\n",
    "outage_ARPD_N_DNN_CF = np.load(path_1+\"outage_ARPD_N_DNN_CF.npz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ce178eb",
   "metadata": {},
   "source": [
    "# Load DF data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a614a7ec",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'stats/db_gap_DF.npz'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_374398/2088638582.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdb_gap_CF\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"db_gap_DF.npz\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mmean_ARDP_CF\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"mean_ARDP_DF.npz\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.8/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    414\u001b[0m             \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    415\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 416\u001b[0;31m             \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstack\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menter_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    417\u001b[0m             \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'stats/db_gap_DF.npz'"
     ]
    }
   ],
   "source": [
    "path_1 = 'stats/'\n",
    "\n",
    "#-----------------------------(Closed form solution)------------------------------------#\n",
    "\n",
    "\n",
    "db_gap_DF = np.load(path_1+\"db_gap_DF.npz\")\n",
    "\n",
    "mean_outage_ARPD_DF = np.load(path_1+\"mean_outage_ARPD_DF.npz\")\n",
    "\n",
    "outage_ARPD_DF = np.load(path_1+\"outage_ARPD_DF.npz\")\n",
    "\n",
    "#-----------------------------(DNN perfect CSI only)------------------------------------#\n",
    "db_gap_DNN_DF = np.load(path_1+\"db_gap_DNN_DF.npz\")\n",
    "\n",
    "mean_outage_ARPD_DNN_DF = np.load(path_1+\"mean_outage_ARPD_DNN_DF.npz\")\n",
    "\n",
    "outage_ARPD_DNN_DF = np.load(path_1+\"outage_ARPD_DNN_DF.npz\")\n",
    "#-----------------------------(DNN robust training)------------------------------------#\n",
    "\n",
    "db_gap_N_DNN_DF = np.load(path_1+\"db_gap_N_DNN_DF.npz\")\n",
    "\n",
    "mean_outage_ARPD_N_DNN_DF = np.load(path_1+\"mean_outage_ARPD_N_DNN_DF.npz\")\n",
    "\n",
    "outage_ARPD_N_DNN_DF = np.load(path_1+\"outage_ARPD_N_DNN_DF.npz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4ef895c",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9dcaa6c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(3, 2,figsize=(15,15), sharex=True, sharey=True)\n",
    "\n",
    "left  = 0.125  # the left side of the subplots of the figure\n",
    "right = 0.9    # the right side of the subplots of the figure\n",
    "bottom = 0.1   # the bottom of the subplots of the figure\n",
    "top = 0.9      # the top of the subplots of the figure\n",
    "wspace = 0.2   # the amount of width reserved for blank space between subplots\n",
    "hspace = 0.2   # the amount of height reserved for white space between subplots\n",
    "\n",
    "ax[0][0].grid()\n",
    "ax[1][0].grid()\n",
    "ax[2][0].grid()\n",
    "\n",
    "\n",
    "ax[0][1].grid()\n",
    "ax[1][1].grid()\n",
    "ax[2][1].grid()\n",
    "\n",
    "\n",
    "ax[0][0].tick_params(axis='x', labelsize=16)\n",
    "ax[0][1].tick_params(axis='x', labelsize=16)\n",
    "\n",
    "\n",
    "ax[1][0].tick_params(axis='x', labelsize=16)\n",
    "ax[1][1].tick_params(axis='x', labelsize=16)\n",
    "\n",
    "\n",
    "ax[2][0].tick_params(axis='x', labelsize=16)\n",
    "ax[2][1].tick_params(axis='x', labelsize=16)\n",
    "\n",
    "\n",
    "ax[0][0].tick_params(axis='y', labelsize=16)\n",
    "ax[0][1].tick_params(axis='y', labelsize=16)\n",
    "\n",
    "\n",
    "ax[1][0].tick_params(axis='y', labelsize=16)\n",
    "ax[1][1].tick_params(axis='y', labelsize=16)\n",
    "\n",
    "\n",
    "ax[2][0].tick_params(axis='y', labelsize=16)\n",
    "ax[2][1].tick_params(axis='y', labelsize=16)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.subplots_adjust(left, bottom, right, top, wspace, hspace)\n",
    "\n",
    "# --------------------------------DF--------------------------------------------------#\n",
    "\n",
    "ax[0][0].set_title('DF Relaying', fontsize=24)\n",
    "\n",
    "# 1 : \n",
    "\n",
    "ax[0][0].plot(SNRs_db, db_gap_DF, c = 'black',label= 'Benchmark', marker = 's',markersize=12)\n",
    "\n",
    "ax[0][0].plot(SNRs_db, db_gap_DNN_DF, c = 'gray',label= 'DNN', marker = '^',markersize=12)\n",
    "\n",
    "ax[0][0].plot(SNRs_db, db_gap_N_DNN_DF, c = 'green',label= 'N-DNN', marker = 'o',markersize=12)\n",
    "\n",
    "# 2 : \n",
    "\n",
    "ax[1][0].plot(SNRs_db, Mean_Outage_ARPD_DF, c = 'black',label= 'Bruteforce (DF)', marker = 's',markersize=12)\n",
    "\n",
    "ax[1][0].plot(SNRs_db, Mean_Outage_ARPD_DNN_DF, c = 'gray',label= 'DNN (DF)', marker = '^',markersize=12)\n",
    "\n",
    "ax[1][0].plot(SNRs_db, mean_outage_ARPD_N_DNN_DF, c = 'green',label= 'N-DNN (DF)', marker = 'o',markersize=12)\n",
    "\n",
    "# 3 : \n",
    "\n",
    "ax[2][0].plot(SNRs_db, Outage_ARPD_DF, c = 'black',label= 'Bruteforce (DF)', marker = 's',markersize=12)\n",
    "\n",
    "ax[2][0].plot(SNRs_db, Outage_ARPD_DNN_DF, c = 'gray',label= 'DNN (DF)', marker = '^',markersize=12)\n",
    "\n",
    "ax[2][0].plot(SNRs_db, outage_ARPD_N_DNN_DF, c = 'green',label= 'N-DNN (DF)', marker = 'o',markersize=12)\n",
    "\n",
    "\n",
    "\n",
    "# -----------------------------------DF-----------------------------------------------#\n",
    "\n",
    "\n",
    "# -----------------------------------CF-----------------------------------------------#\n",
    "\n",
    "ax[0][1].set_title('CF Relaying', fontsize=24)\n",
    "\n",
    "\n",
    "\n",
    "ax[0][1].plot(SNRs_db, db_gap_CF, c = 'black',label= 'Closed-Form (CF)', marker = 's',markersize=12)\n",
    "\n",
    "ax[0][1].plot(SNRs_db, db_gap_DNN_CF, c = 'gray',label= 'DNN (CF)', marker = '^',markersize=12)\n",
    "\n",
    "ax[0][1].plot(SNRs_db, db_gap_N_DNN_CF, c = 'green',label= 'N-DNN (CF)', marker = 'o',markersize=12)\n",
    "\n",
    "\n",
    "\n",
    "ax[1][1].plot(SNRs_db, Mean_Outage_ARPD_CF, c = 'black',label= 'Closed-Form (CF)', marker = 's',markersize=12)\n",
    "\n",
    "ax[1][1].plot(SNRs_db, Mean_Outage_ARPD_DNN_CF, c = 'gray',label= 'DNN (CF)', marker = '^',markersize=12)\n",
    "\n",
    "ax[1][1].plot(SNRs_db, mean_outage_ARPD_N_DNN_CF, c = 'green',label= 'N-DNN (CF)', marker = 'o',markersize=12)\n",
    "\n",
    "\n",
    "\n",
    "ax[2][1].plot(SNRs_db, Outage_ARPD_CF, c = 'black',label= 'Closed-Form (CF)', marker = 's',markersize=12)\n",
    "\n",
    "ax[2][1].plot(SNRs_db, Outage_ARPD_DNN_CF, c = 'gray',label= 'DNN (CF)', marker = '^',markersize=12)\n",
    "\n",
    "ax[2][1].plot(SNRs_db, outage_ARPD_N_DNN_CF, c = 'green',label= 'N-DNN (CF)', marker = 'o',markersize=12)\n",
    "\n",
    "\n",
    "# -----------------------------------CF-----------------------------------------------#\n",
    "\n",
    "ax[0][0].set_ylabel('RAG (%)', fontsize=24)\n",
    "\n",
    "#ax[0][1].set_xlabel('SNR (dB)', fontsize=24) #'Noise variance','Percentage'\n",
    "ax[1][0].set_ylabel('Mean Outage (%)', fontsize=24)\n",
    "\n",
    "#ax[0][2].set_xlabel('SNR (dB)', fontsize=24) #'Noise variance','Percentage'\n",
    "ax[2][0].set_ylabel('Outage (%)', fontsize=24)\n",
    "\n",
    "\n",
    "ax[2][0].set_xlabel('SNR (dB)', fontsize=24) #'Noise variance','Percentage'\n",
    "ax[2][1].set_xlabel('SNR (dB)', fontsize=24) #'Noise variance','Percentage'\n",
    "\n",
    "#ax[0][0].legend(bbox_to_anchor=(2,2), loc=\"upper center\")\n",
    "#ax[0][1].legend(bbox_to_anchor=(2,2), loc=\"upper center\")\n",
    "\n",
    "ax[0][0].legend(bbox_to_anchor=(1.9, -2.65),\n",
    "          fancybox=True, shadow=True, ncol=3, fontsize=24)\n",
    "\n",
    "#ax[0][1].legend(bbox_to_anchor=(1, -2.8),\n",
    "#          fancybox=True, shadow=True, ncol=3, fontsize=14)\n",
    "\n",
    "fig.savefig(\"imperfect_CSI_DF-CF.pdf\", bbox_inches='tight')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05d59441",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}