api/Projects/Project2/Blocking Probability for Different ALs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b3b515c1",
   "metadata": {},
   "source": [
    "# Variation in Blocking Probability with Different Aggregation Levels (ALs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b508fc5",
   "metadata": {},
   "source": [
    "\n",
    "* In this notebook, we simulate the `blocking probability` as a function of different `ALs`.\n",
    "* We consider 5 scenarios corresponding to each AL supported for USS. In each scenario only one of the AL is used the probability of 1. \n",
    "* For instance [1, 0, 0, 0, 0] distribution is used to evaluate the blocking probability when AL 1 is chosen.\n",
    "* Intuitively we see that a higher AL provides a better coverage but at the cost of using more CCEs and hence higher   blocking probabilty.\n",
    "\n",
    "______________________________________________________________________________________________________________\n",
    "* We consider 5 scenarios corresponding to each Aggregation Level (AL) of interest and the distributions are as follows:\n",
    "\n",
    "\n",
    "    * AL 1 distribution :  [1, 0, 0, 0, 0] \n",
    "    * AL 2 distribution :  [0, 1, 0, 0, 0] \n",
    "    * AL 4 distribution :  [0, 0, 1, 0, 0] \n",
    "    * AL 8 distribution :  [0, 0, 0, 1, 0] \n",
    "    * AL 16 distribution : [0, 0, 0, 0, 1]\n",
    "    \n",
    "    \n",
    "* The CORESET size `Nccep` is 54 CCEs for this simulation.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27015050",
   "metadata": {},
   "source": [
    "## Python Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3508fe5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' \n",
    "\n",
    "# %matplotlib widget\n",
    "import matplotlib.pyplot  as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib as mpl\n",
    "\n",
    "import numpy      as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0594c235",
   "metadata": {},
   "source": [
    "## 5G-Toolkit Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af1e89fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../../\")\n",
    "from toolkit5G.Scheduler import PDCCHScheduler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c90aa245",
   "metadata": {},
   "source": [
    "## Simulation Parameters\n",
    "The following parameters are used for this simulation:\n",
    "- `coresetID` denotes the coreset ID.\n",
    "- `slotNumber` denotes the slot-number carrying the PDCCH.\n",
    "- `searchSpaceType` denotes the search space type. UE specific search space (USS) or Common search space (CSS).\n",
    "- `nci` denotes the variable corresponding to carrier aggregation. Current simulation does not assume carrier aggregation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f9589f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "mu                = np.random.randint(4)    # numerlogy for sub-carrier spacing\n",
    "numSlotsPerFrame  = 2**mu * 10              # number of slots per radio frame\n",
    "coresetID         = 1                       # coreset ID\n",
    "slotNumber        = 0                       # slot number\n",
    "searchSpaceType   = \"USS\"                   # search space type. UE specific search space\n",
    "nci               = 0                       # variable corresponding to carrier aggregation\n",
    "numIterations     = 1000                    # Number of Iterations considered in simulation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd9783c6",
   "metadata": {},
   "source": [
    "## PDCCH Scheduling Parameters\n",
    "Following parameters are crucial for PDCCH scheduling performance:\n",
    "- ``Nccep`` denotes coreset size or number of CCEs available for scheduling UEs.\n",
    "- ``strategy`` denotes the scheduling strategy.\n",
    "- ``numCandidates`` denotes number of PDCCH candidates per each Aggregation Level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "588721fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Nccep             = 54                                         # coreset size in terms of CCEs \n",
    "maxNumUEs         = 60\n",
    "numUEs            = np.arange(0,maxNumUEs+1,4)                 # UE range considered in simulation\n",
    "numUEs[0]         = 1\n",
    "numCandidates     = np.array([6,6,4,2,1], dtype=int)           # number of PDCCH candidates per AL\n",
    "strategy          = \"Conservative\"                             # scheduling strategy \n",
    "pdcchSchedulerObj = PDCCHScheduler(mu,slotNumber,coresetID,nci)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0962e71c",
   "metadata": {},
   "source": [
    "####  Impact of AL 1\n",
    "\n",
    "* In this case the Base Station (BS) only configures 6 candidates (of AL 1) for each UE to monitor and other ALs are not monitored. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b206347c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating (n,numUEs) : 0, 1\n",
      "Simulating (n,numUEs) : 1, 4\n",
      "Simulating (n,numUEs) : 2, 8\n",
      "Simulating (n,numUEs) : 3, 12\n",
      "Simulating (n,numUEs) : 4, 16\n",
      "Simulating (n,numUEs) : 5, 20\n",
      "Simulating (n,numUEs) : 6, 24\n",
      "Simulating (n,numUEs) : 7, 28\n",
      "Simulating (n,numUEs) : 8, 32\n",
      "Simulating (n,numUEs) : 9, 36\n",
      "Simulating (n,numUEs) : 10, 40\n",
      "Simulating (n,numUEs) : 11, 44\n",
      "Simulating (n,numUEs) : 12, 48\n",
      "Simulating (n,numUEs) : 13, 52\n",
      "Simulating (n,numUEs) : 14, 56\n",
      "Simulating (n,numUEs) : 15, 60\n"
     ]
    }
   ],
   "source": [
    "########\n",
    "# AL1\n",
    "########\n",
    "aggLevelProbDistributionForAL1 = np.array([1, 0.0, 0.0, 0.0, 0.0])\n",
    "probOfBlockingForAL1 = np.zeros(numUEs.shape)\n",
    "\n",
    "for n in range(numUEs.size):\n",
    "    print(\"Simulating (n,numUEs) : \"+str(n)+\", \"+str(numUEs[n]))\n",
    "    prob      = 0\n",
    "    for i in range(numIterations):\n",
    "        ueALdistribution  = np.random.multinomial(numUEs[n], aggLevelProbDistributionForAL1)\n",
    "        rnti              = np.random.choice( np.arange(1,65519+1), size = (numUEs[n],), replace=False)\n",
    "        count             = pdcchSchedulerObj(Nccep,searchSpaceType,ueALdistribution,numCandidates,rnti,strategy)[0]\n",
    "        numBlockedUEs     = np.sum(count)\n",
    "        prob              = prob + numBlockedUEs/numUEs[n]\n",
    "    probOfBlockingForAL1[n] = prob/numIterations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c42ede",
   "metadata": {},
   "source": [
    "####  Impact of AL 2\n",
    "\n",
    "* In this case the BS only configures 6 candidates (of AL 2) for each UE to monitor and other ALs are not monitored. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16e295ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating (n,numUEs) : 0, 1\n",
      "Simulating (n,numUEs) : 1, 4\n",
      "Simulating (n,numUEs) : 2, 8\n",
      "Simulating (n,numUEs) : 3, 12\n",
      "Simulating (n,numUEs) : 4, 16\n",
      "Simulating (n,numUEs) : 5, 20\n",
      "Simulating (n,numUEs) : 6, 24\n",
      "Simulating (n,numUEs) : 7, 28\n",
      "Simulating (n,numUEs) : 8, 32\n",
      "Simulating (n,numUEs) : 9, 36\n",
      "Simulating (n,numUEs) : 10, 40\n",
      "Simulating (n,numUEs) : 11, 44\n",
      "Simulating (n,numUEs) : 12, 48\n",
      "Simulating (n,numUEs) : 13, 52\n",
      "Simulating (n,numUEs) : 14, 56\n",
      "Simulating (n,numUEs) : 15, 60\n"
     ]
    }
   ],
   "source": [
    "########\n",
    "# AL2\n",
    "########\n",
    "aggLevelProbDistributionForAL2 = np.array([0.0, 1, 0.0, 0.0, 0.0])\n",
    "\n",
    "probOfBlockingForAL2 = np.zeros(numUEs.shape)\n",
    "\n",
    "for n in range(numUEs.size):\n",
    "    print(\"Simulating (n,numUEs) : \"+str(n)+\", \"+str(numUEs[n]))\n",
    "    prob      = 0\n",
    "    for i in range(numIterations):\n",
    "        ueALdistribution  = np.random.multinomial(numUEs[n], aggLevelProbDistributionForAL2)\n",
    "        rnti              = np.random.choice( np.arange(1,65519+1), size = (numUEs[n],), replace=False)\n",
    "        count             = pdcchSchedulerObj(Nccep,searchSpaceType,ueALdistribution,numCandidates,rnti,strategy)[0]\n",
    "        numBlockedUEs     = np.sum(count)\n",
    "        prob              = prob + numBlockedUEs/numUEs[n]\n",
    "    probOfBlockingForAL2[n] = prob/numIterations "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73184d0c",
   "metadata": {},
   "source": [
    "####  Impact of AL 4\n",
    "\n",
    "* In this case the BS only configures 4 candidates (of AL 4) for each UE to monitor and other ALs are not monitored. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9c8b7cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating (n,numUEs) : 0, 1\n",
      "Simulating (n,numUEs) : 1, 4\n",
      "Simulating (n,numUEs) : 2, 8\n",
      "Simulating (n,numUEs) : 3, 12\n",
      "Simulating (n,numUEs) : 4, 16\n",
      "Simulating (n,numUEs) : 5, 20\n",
      "Simulating (n,numUEs) : 6, 24\n",
      "Simulating (n,numUEs) : 7, 28\n",
      "Simulating (n,numUEs) : 8, 32\n",
      "Simulating (n,numUEs) : 9, 36\n",
      "Simulating (n,numUEs) : 10, 40\n",
      "Simulating (n,numUEs) : 11, 44\n",
      "Simulating (n,numUEs) : 12, 48\n",
      "Simulating (n,numUEs) : 13, 52\n",
      "Simulating (n,numUEs) : 14, 56\n",
      "Simulating (n,numUEs) : 15, 60\n"
     ]
    }
   ],
   "source": [
    "########\n",
    "# AL4\n",
    "########\n",
    "aggLevelProbDistributionForAL4 = np.array([0.0, 0.0, 1, 0.0, 0.0])\n",
    "\n",
    "probOfBlockingForAL4 = np.zeros(numUEs.shape)\n",
    "\n",
    "for n in range(numUEs.size):\n",
    "    print(\"Simulating (n,numUEs) : \"+str(n)+\", \"+str(numUEs[n]))\n",
    "    prob      = 0\n",
    "    for i in range(numIterations):\n",
    "        ueALdistribution  = np.random.multinomial(numUEs[n], aggLevelProbDistributionForAL4)\n",
    "        rnti              = np.random.choice( np.arange(1,65519+1), size = (numUEs[n],), replace=False)\n",
    "        count             = pdcchSchedulerObj(Nccep,searchSpaceType,ueALdistribution,numCandidates,rnti,strategy)[0]\n",
    "        numBlockedUEs     = np.sum(count)\n",
    "        prob              = prob + numBlockedUEs/numUEs[n]\n",
    "    probOfBlockingForAL4[n] = prob/numIterations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f424125f",
   "metadata": {},
   "source": [
    "####  Impact of AL 8\n",
    "\n",
    "* In this case the BS only configures 2 candidates (of AL 8) for each UE to monitor and other ALs are not monitored. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7723ab44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating (n,numUEs) : 0, 1\n",
      "Simulating (n,numUEs) : 1, 4\n",
      "Simulating (n,numUEs) : 2, 8\n",
      "Simulating (n,numUEs) : 3, 12\n",
      "Simulating (n,numUEs) : 4, 16\n",
      "Simulating (n,numUEs) : 5, 20\n",
      "Simulating (n,numUEs) : 6, 24\n",
      "Simulating (n,numUEs) : 7, 28\n",
      "Simulating (n,numUEs) : 8, 32\n",
      "Simulating (n,numUEs) : 9, 36\n",
      "Simulating (n,numUEs) : 10, 40\n",
      "Simulating (n,numUEs) : 11, 44\n",
      "Simulating (n,numUEs) : 12, 48\n",
      "Simulating (n,numUEs) : 13, 52\n",
      "Simulating (n,numUEs) : 14, 56\n",
      "Simulating (n,numUEs) : 15, 60\n"
     ]
    }
   ],
   "source": [
    "########\n",
    "# AL8\n",
    "########\n",
    "aggLevelProbDistributionForAL8 = np.array([0.0, 0.0, 0.0, 1, 0.0])\n",
    "\n",
    "probOfBlockingForAL8 = np.zeros(numUEs.shape)\n",
    "\n",
    "for n in range(numUEs.size):\n",
    "    print(\"Simulating (n,numUEs) : \"+str(n)+\", \"+str(numUEs[n]))\n",
    "    prob      = 0\n",
    "    for i in range(numIterations):\n",
    "        ueALdistribution  = np.random.multinomial(numUEs[n], aggLevelProbDistributionForAL8)\n",
    "        rnti              = np.random.choice( np.arange(1,65519+1), size = (numUEs[n],), replace=False)\n",
    "        count             = pdcchSchedulerObj(Nccep,searchSpaceType,ueALdistribution,numCandidates,rnti,strategy)[0]\n",
    "        numBlockedUEs     = np.sum(count)\n",
    "        prob              = prob + numBlockedUEs/numUEs[n]\n",
    "    probOfBlockingForAL8[n] = prob/numIterations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d05ef33",
   "metadata": {},
   "source": [
    "####  Impact of AL 16\n",
    "\n",
    "* In this case the BS only configures 1 candidate (of AL 16) for each UE to monitor and other ALs are not monitored. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dc43eb96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating (n,numUEs) : 0, 1\n",
      "Simulating (n,numUEs) : 1, 4\n",
      "Simulating (n,numUEs) : 2, 8\n",
      "Simulating (n,numUEs) : 3, 12\n",
      "Simulating (n,numUEs) : 4, 16\n",
      "Simulating (n,numUEs) : 5, 20\n",
      "Simulating (n,numUEs) : 6, 24\n",
      "Simulating (n,numUEs) : 7, 28\n",
      "Simulating (n,numUEs) : 8, 32\n",
      "Simulating (n,numUEs) : 9, 36\n",
      "Simulating (n,numUEs) : 10, 40\n",
      "Simulating (n,numUEs) : 11, 44\n",
      "Simulating (n,numUEs) : 12, 48\n",
      "Simulating (n,numUEs) : 13, 52\n",
      "Simulating (n,numUEs) : 14, 56\n",
      "Simulating (n,numUEs) : 15, 60\n"
     ]
    }
   ],
   "source": [
    "########\n",
    "# AL16\n",
    "########\n",
    "aggLevelProbDistributionForAL16 = np.array([0.0, 0.0, 0.0, 0.0, 1])\n",
    "\n",
    "probOfBlockingForAL16 = np.zeros(numUEs.shape)\n",
    "\n",
    "for n in range(numUEs.size):\n",
    "    print(\"Simulating (n,numUEs) : \"+str(n)+\", \"+str(numUEs[n]))\n",
    "    prob      = 0\n",
    "    for i in range(numIterations):\n",
    "        ueALdistribution  = np.random.multinomial(numUEs[n], aggLevelProbDistributionForAL16)\n",
    "        rnti              = np.random.choice( np.arange(1,65519+1), size = (numUEs[n],), replace=False)\n",
    "        count             = pdcchSchedulerObj(Nccep,searchSpaceType,ueALdistribution,numCandidates,rnti,strategy)[0]\n",
    "        numBlockedUEs     = np.sum(count)\n",
    "        prob              = prob + numBlockedUEs/numUEs[n]\n",
    "    probOfBlockingForAL16[n] = prob/numIterations "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33f338c6",
   "metadata": {},
   "source": [
    "## Plot the Variation in Blocking Probability with number of UEs for different Aggregation levels.\n",
    "\n",
    "* Its the recreation of ``Fig. 7: Blocking probability for different ALs (CORESET size = 54 CCEs)``from the           reference   paper referenced below [1].\n",
    "\n",
    "* From the figure we see that using a higher AL leads to higher blocking probability.\n",
    "\n",
    "* The results in figure shows that to maintain the blocking probability `below 0.2`, the maximum possible number of UEs to be scheduled with ALs 2, 4, 8, and 16 is close to 33, 16, 6, and 2 respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0e6d479b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(numUEs, probOfBlockingForAL1,  marker  = \"*\", linestyle = \"solid\", ms = 9, c = 'b', label = \"AL1\")\n",
    "ax.plot(numUEs, probOfBlockingForAL2,  marker  = \"o\", linestyle = \"solid\", ms = 6, c = 'y', label = \"AL2\")\n",
    "ax.plot(numUEs, probOfBlockingForAL4,  marker  = \"v\", linestyle = \"solid\", ms = 6, c = 'k', label = \"AL4\")\n",
    "ax.plot(numUEs, probOfBlockingForAL8,  marker  = \"^\", linestyle = \"solid\", ms = 6, c = 'g', label = \"AL8\")\n",
    "ax.plot(numUEs, probOfBlockingForAL16, marker  = \"s\", linestyle = \"solid\", ms = 6, c = 'm', label = \"AL16\")\n",
    "ax.legend()\n",
    "\n",
    "ax.set_xlabel('Total Number of Scheduled UEs')\n",
    "ax.set_ylabel('Blocking Probability')\n",
    "ax.set_title('Blocking Probability for different ALs', fontsize=12) \n",
    "ax.set_xticks(numUEs[0:-1:2])\n",
    "ax.set_yticks(np.linspace(0.05,0.95,10), minor=True)\n",
    "ax.set_yticks(np.linspace(0,   1,11), minor=False)\n",
    "ax.tick_params(axis='both',which='major', grid_linewidth=1, width=0)\n",
    "ax.tick_params(axis='both',which='minor', grid_linewidth=0.5, grid_linestyle = '--')    \n",
    "ax.set_ylim([0, 1])\n",
    "ax.set_xlim([0, numUEs[-1]])\n",
    "\n",
    "ax.grid(which='both')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb832387",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b230897f",
   "metadata": {},
   "source": [
    "[1] `Blocking Probability Analysis for 5G New Radio (NR) Physical Downlink Control Channel`. Mohammad Mozaffari,\n",
    "      Y.-P. Eric Wang, and Kittipong Kittichokechai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8e42f51",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}