api/Tutorials/Tutorial21+/[SLS]Positioning_in_IndoorFactory_5G_Networks_based_on_UL-TDoA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ee902694",
   "metadata": {},
   "source": [
    "# Positioning the Outdoor UEs using 5G Urban Micro cell sites based Uplink Time Difference of Arrival (UL-TDoA) method\n",
    "\n",
    "\n",
    "Uplink Time Difference of Arrival (UL-TDoA) positioning in 5G networks is a technique used to determine the location of a user equipment (UE) by measuring the time difference of arrival of signals transmitted from multiple base stations (gNBs). Here's an overview of UL-TDoA based positioning in 5G networks:\n",
    "\n",
    "1. **Principle**: UL-TDoA positioning relies on the principle that signals transmitted from the UE reach multiple gNBs at slightly different times due to variations in signal propagation delays. By accurately measuring these time differences of arrival at the gNBs, the UE's location can be determined using trilateration or multilateration techniques.\n",
    "\n",
    "2. **Reference Signals**: To enable UL-TDoA positioning, the 5G network periodically transmits reference signals from gNBs with known timing and synchronization. These reference signals are received by the UE and used to accurately measure the time of arrival at each gNB.\n",
    "\n",
    "3. **Measurement Procedure**: When the UE needs to determine its position, it measures the time of arrival of the reference signals from multiple neighboring gNBs. These time measurements are typically made using high-precision timing mechanisms, such as GPS or atomic clocks, to achieve sub-meter or even centimeter-level accuracy.\n",
    "\n",
    "4. **Time Difference Calculation**: Once the time of arrival measurements are obtained from multiple gNBs, the UE calculates the time differences between pairs of gNBs. These time differences are then converted into distance differences using the known speed of light in the propagation medium.\n",
    "\n",
    "5. **Position Calculation**: Using the distance differences obtained from the time differences of arrival, along with the known locations of the gNBs, the UE's position can be calculated using trilateration or multilateration algorithms. These algorithms estimate the UE's location by intersecting spheres or hyperboloids corresponding to the distance differences from each gNB.\n",
    "\n",
    "6. **Challenges and Considerations**: UL-TDoA positioning in 5G networks faces challenges such as multipath propagation, non-line-of-sight (NLOS) conditions, synchronization errors, and signal interference. Advanced signal processing techniques and robust algorithms are employed to mitigate these challenges and improve the accuracy and reliability of positioning estimates.\n",
    "\n",
    "\n",
    "Overall, UL-TDoA based positioning in 5G networks offers ultra-accurate location determination capabilities, making it suitable for applications requiring precise positioning, such as emergency services, asset tracking, and indoor navigation.\n",
    "\n",
    "\n",
    "**Table of Content**\n",
    "\n",
    "* [Import Libraries](#Import-Libraries)\n",
    "    * [Import Basic Python Libraries](#Import-Basic-Python-Libraries)\n",
    "    * [Import 5G Toolkit Modules](#Import-5G-Toolkit-Libraries)\n",
    "* [Learning to Demap the Symbols](#Learning-to-Demap-the-Symbols)\n",
    "* [Simulation Parameters](#Simulation-Parameters)\n",
    "* [Generate Wireless Channels](#Generate-Wireless-Channels)\n",
    "* [SRS Configurations](#SRS-Configurations)\n",
    "* [Slot by Slot Simulation](#Slot-by-Slot-Simulation)\n",
    "* [Position Estimation: Based on UL ToA](#Position-Estimation:-Based-on-UL-ToA)\n",
    "* [Visualization of Estimated Position](#Visualization-of-Estimated-Position)\n",
    "* [Performance Analysis of Positioning Error for ToA based method](#Performance-Analysis-of-Positioning-Error-for-ToA-based-method)\n",
    "* [Performance Analysis: For 2000 UEs](#Performance-Analysis:-For-2000-UEs)\n",
    "\n",
    "\n",
    "\n",
    "## Import Libraries\n",
    "\n",
    "### Import Basic Python Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a6b3d99c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from IPython.display import display, HTML\n",
    "# display(HTML(\"<style>.container { width:90% !important; }</style>\"))\n",
    "\n",
    "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\n",
    "import numpy.matlib\n",
    "import scipy      as sp\n",
    "import scipy.io   as spio\n",
    "import scipy.constants\n",
    "from   scipy import interpolate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b83feb7c",
   "metadata": {},
   "source": [
    "### Import 5G Toolkit Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "058d21a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../../../\")\n",
    "\n",
    "from toolkit5G.ChannelModels      import AntennaArrays, SimulationLayout, ParameterGenerator, ChannelGenerator\n",
    "from toolkit5G.ResourceMapping    import ResourceMapperSRS\n",
    "from toolkit5G.ReceiverAlgorithms import ChannelEstimationSRS\n",
    "from toolkit5G.Positioning        import ToAEstimation, PositionEstimation, LeastSquareTDoA, LeastSquareToA\n",
    "from toolkit5G.ChannelProcessing  import AddNoise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e98ac53",
   "metadata": {},
   "source": [
    "## Simulation Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34e24947",
   "metadata": {},
   "source": [
    "\n",
    "|Parameters|Values|\n",
    "| :- | -:  |\n",
    "| Positioning Method  | UL-TDoA  |\n",
    "| Parameter Estimation Method  | ESPRIT  |\n",
    "| Optimization Method | Least Squares |\n",
    "| Carrier Frequency | 3.6 GHz |\n",
    "| Bandwidth | 100 MHz |\n",
    "| Subcarrier Spacing | 300 kHz|\n",
    "| Terrain | Indoor-Factory (UMi) |\n",
    "|Channel State Information |Zero Forcing + Spline Interpolation|\n",
    "| Reference Signal | Sounding Reference Signal for Positioning (SRS-P) |\n",
    "|Simulation Type| System Level Simulation|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "35294d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Simulation Parameters\n",
    "\n",
    "propTerrain      = \"UMi\"         # Propagation Scenario or Terrain for BS-UE links\n",
    "carrierFrequency = 3.6*10**9         # Array of two carrier frequencies in GHz\n",
    "scs              = 30*10**3\n",
    "Nfft             = 4096\n",
    "numOfBSs         = 21 # number of BSs\n",
    "nBSs             = np.prod(numOfBSs)\n",
    "nUEs             = 128             # number of UEs\n",
    "numRBs           = 272\n",
    "numSlots         = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b50880c",
   "metadata": {},
   "source": [
    "## Generate Wireless Channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "05adfa29",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Warning]: Pathloss model for UMi is defined only for  2D distances 'd2D' between 10m and 5Km! Some distances are from outside this interval!\n",
      "Ignoring for now but might result in unexpected behaviour!\n",
      "Channel Parameters Generated!\n",
      "Channel Coefficient Generated!\n",
      "OFDM Channel Generated!\n"
     ]
    }
   ],
   "source": [
    "## Generate the Wireless Channel\n",
    "\n",
    "# Antenna Array at UE side\n",
    "# assuming antenna element type to be \"OMNI\"\n",
    "# with 2 panel and 2 single polarized antenna element per panel.\n",
    "ueAntArray = AntennaArrays(antennaType = \"OMNI\",  centerFrequency = carrierFrequency, arrayStructure  = np.array([1,1,2,2,1]))\n",
    "ueAntArray()\n",
    "\n",
    "# # Radiation Pattern of Rx antenna element\n",
    "# ueAntArray.displayAntennaRadiationPattern()\n",
    "\n",
    "\n",
    "# Antenna Array at BS side\n",
    "# assuming antenna element type to be \"3GPP_38.901\", a parabolic antenna\n",
    "# with 4 panel and 4 single polarized antenna element per panel.\n",
    "bsAntArray = AntennaArrays(antennaType = \"3GPP_38.901\", centerFrequency = carrierFrequency, arrayStructure  = np.array([1,1,8,4,1]))\n",
    "bsAntArray()\n",
    "\n",
    "# # Radiation Pattern of Tx antenna element\n",
    "# bsAntArray[0].displayAntennaRadiationPattern()\n",
    "\n",
    "# Layout Parameters\n",
    "isd                  = 100          # inter site distance\n",
    "minDist              = 10          # min distance between each UE and BS\n",
    "ueHt                 = 1.5         # UE height\n",
    "bsHt                 = 10          # BS height\n",
    "bslayoutType         = \"Hexagonal\" # BS layout type\n",
    "ueDropType           = \"Hexagonal\" # UE drop type\n",
    "htDist               = \"equal\"       # UE height distribution\n",
    "ueDist               = \"random\"       # UE Distribution per site\n",
    "nSectorsPerSite      = 3             # number of sectors per site\n",
    "maxNumFloors         = 1             # Max number of floors in an indoor object\n",
    "minNumFloors         = 1             # Min number of floors in an indoor object\n",
    "heightOfRoom         = 5.1             # height of room or ceiling in meters\n",
    "indoorUEfract        = 0.5           # Fraction of UEs located indoor\n",
    "lengthOfIndoorObject = 3             # length of indoor object typically having rectangular geometry\n",
    "widthOfIndoorObject  = 3             # width of indoor object\n",
    "forceLOS             = True          # boolen flag if true forces every link to be in LOS state\n",
    "# forceLOS             = False       # boolen flag if true forces every link to be in LOS state\n",
    "\n",
    "# simulation layout object\n",
    "simLayoutObj = SimulationLayout(numOfBS = numOfBSs,\n",
    "                                numOfUE = nUEs,\n",
    "                                heightOfBS = bsHt,\n",
    "                                heightOfUE = ueHt,\n",
    "                                ISD = isd,\n",
    "                                layoutType = bslayoutType,\n",
    "                                layoutWidth = 30,\n",
    "                                layoutLength = 120,\n",
    "                                ueDropMethod = ueDropType,\n",
    "                                UEdistibution = ueDist,\n",
    "                                UEheightDistribution = htDist,\n",
    "                                numOfSectorsPerSite = nSectorsPerSite,\n",
    "                                ueRoute = None)\n",
    "\n",
    "simLayoutObj(terrain = propTerrain,\n",
    "             carrierFreq = carrierFrequency,\n",
    "             ueAntennaArray = ueAntArray,\n",
    "             bsAntennaArray = bsAntArray,\n",
    "             indoorUEfraction = indoorUEfract,\n",
    "             heightOfRoom = heightOfRoom,\n",
    "             lengthOfIndoorObject = lengthOfIndoorObject,\n",
    "             widthOfIndoorObject = widthOfIndoorObject,\n",
    "             forceLOS = forceLOS)\n",
    "\n",
    "# displaying the topology of simulation layout\n",
    "fig, ax = simLayoutObj.display2DTopology()\n",
    "ax.set_xlabel(\"x-coordinates (m)\")\n",
    "ax.set_ylabel(\"y-coordinates (m)\")\n",
    "ax.set_title(\"Simulation Topology\")\n",
    "# ax.axhline(y=-0.5*isd*numOfBSs[1], xmin=10/140, xmax=130/140, color=\"k\")\n",
    "# ax.axhline(y= 0.5*isd*numOfBSs[1], xmin=10/140, xmax=130/140, color=\"k\")\n",
    "# ax.axvline(x=-0.5*isd*numOfBSs[0], ymin=10/140, ymax=130/140, color=\"k\")\n",
    "# ax.axvline(x= 0.5*isd*numOfBSs[0], ymin=10/140, ymax=130/140, color=\"k\")\n",
    "\n",
    "paramGen = simLayoutObj.getParameterGenerator()\n",
    "print(\"Channel Parameters Generated!\")\n",
    "\n",
    "# paramGen.displayClusters((0,0,0), rayIndex = 0)\n",
    "channel = paramGen.getChannel()\n",
    "print(\"Channel Coefficient Generated!\")\n",
    "Hf      = channel.ofdm(scs, Nfft)[0]\n",
    "print(\"OFDM Channel Generated!\")\n",
    "\n",
    "Nt        = bsAntArray.numAntennas # Number of BS Antennas\n",
    "Nr        = ueAntArray.numAntennas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f99164",
   "metadata": {},
   "source": [
    "## SRS Configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d87e4cec",
   "metadata": {},
   "outputs": [],
   "source": [
    "## SRS Configurations\n",
    "purpose\t\t\t    = \"positioning\"\n",
    "nrofSRS_Ports\t\t= 1\n",
    "transmissionComb\t= 4\n",
    "nrofSymbols\t\t    = 12\n",
    "startPosition\t\t= 2\n",
    "repetitionFactor\t= 1\n",
    "nrOfCyclicShift\t\t= 1\n",
    "groupOrSequenceHopping\t= \"neither\"\n",
    "sequenceId\t\t        = np.arange(nUEs)\n",
    "\n",
    "systemFrameNumber\t    = 0\n",
    "resourceType\t\t    = \"periodic\"\n",
    "subcarrierSpacing \t    = scs\n",
    "\n",
    "bSRS \t\t\t        = 0\n",
    "cSRS \t\t\t        = 61\n",
    "bHop \t\t\t        = 0\n",
    "\n",
    "freqScalingFactor \t    = 1\n",
    "startRBIndex \t\t    = 0\n",
    "enableStartRBHopping \t= False\n",
    "freqDomainShift \t    = 0\n",
    "freqDomainPosition \t    = 0\n",
    "srsPeriodicityInSlots \t= 1\n",
    "srsOffsetInSlots \t\t= 0\n",
    "betaSRS \t\t        = 1\n",
    "resourceGridSizeinRBs \t= numRBs\n",
    "Bandwidth               = resourceGridSizeinRBs*12*scs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4340158f",
   "metadata": {},
   "source": [
    "## Slot by Slot Simulation\n",
    "\n",
    "- Schedule a certain UEs for SRS transmission in each slot.\n",
    "- Beamform the slot Grid.\n",
    "- Pass the beamformed Grid through the wireless channel.\n",
    "    - Consider inter-user interference.\n",
    "- Extract the resource Grid.\n",
    "- Estimate the channel between the scheduled users and each BS.\n",
    "    - Estimate the channel using LS estimator.\n",
    "    - Interpolate the channel for un-scheduled REs in the slot Grid.\n",
    "- Estimate the time of arrival (ToA) for each UE-BS link."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d19044a8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***********  Transmission Grid Beamformed *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [0]-Passed Through Channel *********** \n",
      "***********  [0]-Noise Added *********** \n",
      "***********  [0]-Resource Grid Extracted *********** \n",
      "UE-Index: 0 | slot-Index: 0\n",
      "UE-Index: 1 | slot-Index: 0\n",
      "UE-Index: 2 | slot-Index: 0\n",
      "UE-Index: 3 | slot-Index: 0\n",
      "***********  [0]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 0)\n",
      "(nbs, nue): (0, 1)\n",
      "(nbs, nue): (0, 2)\n",
      "(nbs, nue): (0, 3)\n",
      "(nbs, nue): (1, 0)\n",
      "(nbs, nue): (1, 1)\n",
      "(nbs, nue): (1, 2)\n",
      "(nbs, nue): (1, 3)\n",
      "(nbs, nue): (2, 0)\n",
      "(nbs, nue): (2, 1)\n",
      "(nbs, nue): (2, 2)\n",
      "(nbs, nue): (2, 3)\n",
      "(nbs, nue): (3, 0)\n",
      "(nbs, nue): (3, 1)\n",
      "(nbs, nue): (3, 2)\n",
      "(nbs, nue): (3, 3)\n",
      "(nbs, nue): (4, 0)\n",
      "(nbs, nue): (4, 1)\n",
      "(nbs, nue): (4, 2)\n",
      "(nbs, nue): (4, 3)\n",
      "(nbs, nue): (5, 0)\n",
      "(nbs, nue): (5, 1)\n",
      "(nbs, nue): (5, 2)\n",
      "(nbs, nue): (5, 3)\n",
      "(nbs, nue): (6, 0)\n",
      "(nbs, nue): (6, 1)\n",
      "(nbs, nue): (6, 2)\n",
      "(nbs, nue): (6, 3)\n",
      "(nbs, nue): (7, 0)\n",
      "(nbs, nue): (7, 1)\n",
      "(nbs, nue): (7, 2)\n",
      "(nbs, nue): (7, 3)\n",
      "(nbs, nue): (8, 0)\n",
      "(nbs, nue): (8, 1)\n",
      "(nbs, nue): (8, 2)\n",
      "(nbs, nue): (8, 3)\n",
      "(nbs, nue): (9, 0)\n",
      "(nbs, nue): (9, 1)\n",
      "(nbs, nue): (9, 2)\n",
      "(nbs, nue): (9, 3)\n",
      "(nbs, nue): (10, 0)\n",
      "(nbs, nue): (10, 1)\n",
      "(nbs, nue): (10, 2)\n",
      "(nbs, nue): (10, 3)\n",
      "(nbs, nue): (11, 0)\n",
      "(nbs, nue): (11, 1)\n",
      "(nbs, nue): (11, 2)\n",
      "(nbs, nue): (11, 3)\n",
      "(nbs, nue): (12, 0)\n",
      "(nbs, nue): (12, 1)\n",
      "(nbs, nue): (12, 2)\n",
      "(nbs, nue): (12, 3)\n",
      "(nbs, nue): (13, 0)\n",
      "(nbs, nue): (13, 1)\n",
      "(nbs, nue): (13, 2)\n",
      "(nbs, nue): (13, 3)\n",
      "(nbs, nue): (14, 0)\n",
      "(nbs, nue): (14, 1)\n",
      "(nbs, nue): (14, 2)\n",
      "(nbs, nue): (14, 3)\n",
      "(nbs, nue): (15, 0)\n",
      "(nbs, nue): (15, 1)\n",
      "(nbs, nue): (15, 2)\n",
      "(nbs, nue): (15, 3)\n",
      "(nbs, nue): (16, 0)\n",
      "(nbs, nue): (16, 1)\n",
      "(nbs, nue): (16, 2)\n",
      "(nbs, nue): (16, 3)\n",
      "(nbs, nue): (17, 0)\n",
      "(nbs, nue): (17, 1)\n",
      "(nbs, nue): (17, 2)\n",
      "(nbs, nue): (17, 3)\n",
      "(nbs, nue): (18, 0)\n",
      "(nbs, nue): (18, 1)\n",
      "(nbs, nue): (18, 2)\n",
      "(nbs, nue): (18, 3)\n",
      "(nbs, nue): (19, 0)\n",
      "(nbs, nue): (19, 1)\n",
      "(nbs, nue): (19, 2)\n",
      "(nbs, nue): (19, 3)\n",
      "(nbs, nue): (20, 0)\n",
      "(nbs, nue): (20, 1)\n",
      "(nbs, nue): (20, 2)\n",
      "(nbs, nue): (20, 3)\n",
      "***********  [0]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [1]-Passed Through Channel *********** \n",
      "***********  [1]-Noise Added *********** \n",
      "***********  [1]-Resource Grid Extracted *********** \n",
      "UE-Index: 4 | slot-Index: 1\n",
      "UE-Index: 5 | slot-Index: 1\n",
      "UE-Index: 6 | slot-Index: 1\n",
      "UE-Index: 7 | slot-Index: 1\n",
      "***********  [1]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 4)\n",
      "(nbs, nue): (0, 5)\n",
      "(nbs, nue): (0, 6)\n",
      "(nbs, nue): (0, 7)\n",
      "(nbs, nue): (1, 4)\n",
      "(nbs, nue): (1, 5)\n",
      "(nbs, nue): (1, 6)\n",
      "(nbs, nue): (1, 7)\n",
      "(nbs, nue): (2, 4)\n",
      "(nbs, nue): (2, 5)\n",
      "(nbs, nue): (2, 6)\n",
      "(nbs, nue): (2, 7)\n",
      "(nbs, nue): (3, 4)\n",
      "(nbs, nue): (3, 5)\n",
      "(nbs, nue): (3, 6)\n",
      "(nbs, nue): (3, 7)\n",
      "(nbs, nue): (4, 4)\n",
      "(nbs, nue): (4, 5)\n",
      "(nbs, nue): (4, 6)\n",
      "(nbs, nue): (4, 7)\n",
      "(nbs, nue): (5, 4)\n",
      "(nbs, nue): (5, 5)\n",
      "(nbs, nue): (5, 6)\n",
      "(nbs, nue): (5, 7)\n",
      "(nbs, nue): (6, 4)\n",
      "(nbs, nue): (6, 5)\n",
      "(nbs, nue): (6, 6)\n",
      "(nbs, nue): (6, 7)\n",
      "(nbs, nue): (7, 4)\n",
      "(nbs, nue): (7, 5)\n",
      "(nbs, nue): (7, 6)\n",
      "(nbs, nue): (7, 7)\n",
      "(nbs, nue): (8, 4)\n",
      "(nbs, nue): (8, 5)\n",
      "(nbs, nue): (8, 6)\n",
      "(nbs, nue): (8, 7)\n",
      "(nbs, nue): (9, 4)\n",
      "(nbs, nue): (9, 5)\n",
      "(nbs, nue): (9, 6)\n",
      "(nbs, nue): (9, 7)\n",
      "(nbs, nue): (10, 4)\n",
      "(nbs, nue): (10, 5)\n",
      "(nbs, nue): (10, 6)\n",
      "(nbs, nue): (10, 7)\n",
      "(nbs, nue): (11, 4)\n",
      "(nbs, nue): (11, 5)\n",
      "(nbs, nue): (11, 6)\n",
      "(nbs, nue): (11, 7)\n",
      "(nbs, nue): (12, 4)\n",
      "(nbs, nue): (12, 5)\n",
      "(nbs, nue): (12, 6)\n",
      "(nbs, nue): (12, 7)\n",
      "(nbs, nue): (13, 4)\n",
      "(nbs, nue): (13, 5)\n",
      "(nbs, nue): (13, 6)\n",
      "(nbs, nue): (13, 7)\n",
      "(nbs, nue): (14, 4)\n",
      "(nbs, nue): (14, 5)\n",
      "(nbs, nue): (14, 6)\n",
      "(nbs, nue): (14, 7)\n",
      "(nbs, nue): (15, 4)\n",
      "(nbs, nue): (15, 5)\n",
      "(nbs, nue): (15, 6)\n",
      "(nbs, nue): (15, 7)\n",
      "(nbs, nue): (16, 4)\n",
      "(nbs, nue): (16, 5)\n",
      "(nbs, nue): (16, 6)\n",
      "(nbs, nue): (16, 7)\n",
      "(nbs, nue): (17, 4)\n",
      "(nbs, nue): (17, 5)\n",
      "(nbs, nue): (17, 6)\n",
      "(nbs, nue): (17, 7)\n",
      "(nbs, nue): (18, 4)\n",
      "(nbs, nue): (18, 5)\n",
      "(nbs, nue): (18, 6)\n",
      "(nbs, nue): (18, 7)\n",
      "(nbs, nue): (19, 4)\n",
      "(nbs, nue): (19, 5)\n",
      "(nbs, nue): (19, 6)\n",
      "(nbs, nue): (19, 7)\n",
      "(nbs, nue): (20, 4)\n",
      "(nbs, nue): (20, 5)\n",
      "(nbs, nue): (20, 6)\n",
      "(nbs, nue): (20, 7)\n",
      "***********  [1]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [2]-Passed Through Channel *********** \n",
      "***********  [2]-Noise Added *********** \n",
      "***********  [2]-Resource Grid Extracted *********** \n",
      "UE-Index: 8 | slot-Index: 2\n",
      "UE-Index: 9 | slot-Index: 2\n",
      "UE-Index: 10 | slot-Index: 2\n",
      "UE-Index: 11 | slot-Index: 2\n",
      "***********  [2]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 8)\n",
      "(nbs, nue): (0, 9)\n",
      "(nbs, nue): (0, 10)\n",
      "(nbs, nue): (0, 11)\n",
      "(nbs, nue): (1, 8)\n",
      "(nbs, nue): (1, 9)\n",
      "(nbs, nue): (1, 10)\n",
      "(nbs, nue): (1, 11)\n",
      "(nbs, nue): (2, 8)\n",
      "(nbs, nue): (2, 9)\n",
      "(nbs, nue): (2, 10)\n",
      "(nbs, nue): (2, 11)\n",
      "(nbs, nue): (3, 8)\n",
      "(nbs, nue): (3, 9)\n",
      "(nbs, nue): (3, 10)\n",
      "(nbs, nue): (3, 11)\n",
      "(nbs, nue): (4, 8)\n",
      "(nbs, nue): (4, 9)\n",
      "(nbs, nue): (4, 10)\n",
      "(nbs, nue): (4, 11)\n",
      "(nbs, nue): (5, 8)\n",
      "(nbs, nue): (5, 9)\n",
      "(nbs, nue): (5, 10)\n",
      "(nbs, nue): (5, 11)\n",
      "(nbs, nue): (6, 8)\n",
      "(nbs, nue): (6, 9)\n",
      "(nbs, nue): (6, 10)\n",
      "(nbs, nue): (6, 11)\n",
      "(nbs, nue): (7, 8)\n",
      "(nbs, nue): (7, 9)\n",
      "(nbs, nue): (7, 10)\n",
      "(nbs, nue): (7, 11)\n",
      "(nbs, nue): (8, 8)\n",
      "(nbs, nue): (8, 9)\n",
      "(nbs, nue): (8, 10)\n",
      "(nbs, nue): (8, 11)\n",
      "(nbs, nue): (9, 8)\n",
      "(nbs, nue): (9, 9)\n",
      "(nbs, nue): (9, 10)\n",
      "(nbs, nue): (9, 11)\n",
      "(nbs, nue): (10, 8)\n",
      "(nbs, nue): (10, 9)\n",
      "(nbs, nue): (10, 10)\n",
      "(nbs, nue): (10, 11)\n",
      "(nbs, nue): (11, 8)\n",
      "(nbs, nue): (11, 9)\n",
      "(nbs, nue): (11, 10)\n",
      "(nbs, nue): (11, 11)\n",
      "(nbs, nue): (12, 8)\n",
      "(nbs, nue): (12, 9)\n",
      "(nbs, nue): (12, 10)\n",
      "(nbs, nue): (12, 11)\n",
      "(nbs, nue): (13, 8)\n",
      "(nbs, nue): (13, 9)\n",
      "(nbs, nue): (13, 10)\n",
      "(nbs, nue): (13, 11)\n",
      "(nbs, nue): (14, 8)\n",
      "(nbs, nue): (14, 9)\n",
      "(nbs, nue): (14, 10)\n",
      "(nbs, nue): (14, 11)\n",
      "(nbs, nue): (15, 8)\n",
      "(nbs, nue): (15, 9)\n",
      "(nbs, nue): (15, 10)\n",
      "(nbs, nue): (15, 11)\n",
      "(nbs, nue): (16, 8)\n",
      "(nbs, nue): (16, 9)\n",
      "(nbs, nue): (16, 10)\n",
      "(nbs, nue): (16, 11)\n",
      "(nbs, nue): (17, 8)\n",
      "(nbs, nue): (17, 9)\n",
      "(nbs, nue): (17, 10)\n",
      "(nbs, nue): (17, 11)\n",
      "(nbs, nue): (18, 8)\n",
      "(nbs, nue): (18, 9)\n",
      "(nbs, nue): (18, 10)\n",
      "(nbs, nue): (18, 11)\n",
      "(nbs, nue): (19, 8)\n",
      "(nbs, nue): (19, 9)\n",
      "(nbs, nue): (19, 10)\n",
      "(nbs, nue): (19, 11)\n",
      "(nbs, nue): (20, 8)\n",
      "(nbs, nue): (20, 9)\n",
      "(nbs, nue): (20, 10)\n",
      "(nbs, nue): (20, 11)\n",
      "***********  [2]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [3]-Passed Through Channel *********** \n",
      "***********  [3]-Noise Added *********** \n",
      "***********  [3]-Resource Grid Extracted *********** \n",
      "UE-Index: 12 | slot-Index: 3\n",
      "UE-Index: 13 | slot-Index: 3\n",
      "UE-Index: 14 | slot-Index: 3\n",
      "UE-Index: 15 | slot-Index: 3\n",
      "***********  [3]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 12)\n",
      "(nbs, nue): (0, 13)\n",
      "(nbs, nue): (0, 14)\n",
      "(nbs, nue): (0, 15)\n",
      "(nbs, nue): (1, 12)\n",
      "(nbs, nue): (1, 13)\n",
      "(nbs, nue): (1, 14)\n",
      "(nbs, nue): (1, 15)\n",
      "(nbs, nue): (2, 12)\n",
      "(nbs, nue): (2, 13)\n",
      "(nbs, nue): (2, 14)\n",
      "(nbs, nue): (2, 15)\n",
      "(nbs, nue): (3, 12)\n",
      "(nbs, nue): (3, 13)\n",
      "(nbs, nue): (3, 14)\n",
      "(nbs, nue): (3, 15)\n",
      "(nbs, nue): (4, 12)\n",
      "(nbs, nue): (4, 13)\n",
      "(nbs, nue): (4, 14)\n",
      "(nbs, nue): (4, 15)\n",
      "(nbs, nue): (5, 12)\n",
      "(nbs, nue): (5, 13)\n",
      "(nbs, nue): (5, 14)\n",
      "(nbs, nue): (5, 15)\n",
      "(nbs, nue): (6, 12)\n",
      "(nbs, nue): (6, 13)\n",
      "(nbs, nue): (6, 14)\n",
      "(nbs, nue): (6, 15)\n",
      "(nbs, nue): (7, 12)\n",
      "(nbs, nue): (7, 13)\n",
      "(nbs, nue): (7, 14)\n",
      "(nbs, nue): (7, 15)\n",
      "(nbs, nue): (8, 12)\n",
      "(nbs, nue): (8, 13)\n",
      "(nbs, nue): (8, 14)\n",
      "(nbs, nue): (8, 15)\n",
      "(nbs, nue): (9, 12)\n",
      "(nbs, nue): (9, 13)\n",
      "(nbs, nue): (9, 14)\n",
      "(nbs, nue): (9, 15)\n",
      "(nbs, nue): (10, 12)\n",
      "(nbs, nue): (10, 13)\n",
      "(nbs, nue): (10, 14)\n",
      "(nbs, nue): (10, 15)\n",
      "(nbs, nue): (11, 12)\n",
      "(nbs, nue): (11, 13)\n",
      "(nbs, nue): (11, 14)\n",
      "(nbs, nue): (11, 15)\n",
      "(nbs, nue): (12, 12)\n",
      "(nbs, nue): (12, 13)\n",
      "(nbs, nue): (12, 14)\n",
      "(nbs, nue): (12, 15)\n",
      "(nbs, nue): (13, 12)\n",
      "(nbs, nue): (13, 13)\n",
      "(nbs, nue): (13, 14)\n",
      "(nbs, nue): (13, 15)\n",
      "(nbs, nue): (14, 12)\n",
      "(nbs, nue): (14, 13)\n",
      "(nbs, nue): (14, 14)\n",
      "(nbs, nue): (14, 15)\n",
      "(nbs, nue): (15, 12)\n",
      "(nbs, nue): (15, 13)\n",
      "(nbs, nue): (15, 14)\n",
      "(nbs, nue): (15, 15)\n",
      "(nbs, nue): (16, 12)\n",
      "(nbs, nue): (16, 13)\n",
      "(nbs, nue): (16, 14)\n",
      "(nbs, nue): (16, 15)\n",
      "(nbs, nue): (17, 12)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(nbs, nue): (17, 13)\n",
      "(nbs, nue): (17, 14)\n",
      "(nbs, nue): (17, 15)\n",
      "(nbs, nue): (18, 12)\n",
      "(nbs, nue): (18, 13)\n",
      "(nbs, nue): (18, 14)\n",
      "(nbs, nue): (18, 15)\n",
      "(nbs, nue): (19, 12)\n",
      "(nbs, nue): (19, 13)\n",
      "(nbs, nue): (19, 14)\n",
      "(nbs, nue): (19, 15)\n",
      "(nbs, nue): (20, 12)\n",
      "(nbs, nue): (20, 13)\n",
      "(nbs, nue): (20, 14)\n",
      "(nbs, nue): (20, 15)\n",
      "***********  [3]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [4]-Passed Through Channel *********** \n",
      "***********  [4]-Noise Added *********** \n",
      "***********  [4]-Resource Grid Extracted *********** \n",
      "UE-Index: 16 | slot-Index: 4\n",
      "UE-Index: 17 | slot-Index: 4\n",
      "UE-Index: 18 | slot-Index: 4\n",
      "UE-Index: 19 | slot-Index: 4\n",
      "***********  [4]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 16)\n",
      "(nbs, nue): (0, 17)\n",
      "(nbs, nue): (0, 18)\n",
      "(nbs, nue): (0, 19)\n",
      "(nbs, nue): (1, 16)\n",
      "(nbs, nue): (1, 17)\n",
      "(nbs, nue): (1, 18)\n",
      "(nbs, nue): (1, 19)\n",
      "(nbs, nue): (2, 16)\n",
      "(nbs, nue): (2, 17)\n",
      "(nbs, nue): (2, 18)\n",
      "(nbs, nue): (2, 19)\n",
      "(nbs, nue): (3, 16)\n",
      "(nbs, nue): (3, 17)\n",
      "(nbs, nue): (3, 18)\n",
      "(nbs, nue): (3, 19)\n",
      "(nbs, nue): (4, 16)\n",
      "(nbs, nue): (4, 17)\n",
      "(nbs, nue): (4, 18)\n",
      "(nbs, nue): (4, 19)\n",
      "(nbs, nue): (5, 16)\n",
      "(nbs, nue): (5, 17)\n",
      "(nbs, nue): (5, 18)\n",
      "(nbs, nue): (5, 19)\n",
      "(nbs, nue): (6, 16)\n",
      "(nbs, nue): (6, 17)\n",
      "(nbs, nue): (6, 18)\n",
      "(nbs, nue): (6, 19)\n",
      "(nbs, nue): (7, 16)\n",
      "(nbs, nue): (7, 17)\n",
      "(nbs, nue): (7, 18)\n",
      "(nbs, nue): (7, 19)\n",
      "(nbs, nue): (8, 16)\n",
      "(nbs, nue): (8, 17)\n",
      "(nbs, nue): (8, 18)\n",
      "(nbs, nue): (8, 19)\n",
      "(nbs, nue): (9, 16)\n",
      "(nbs, nue): (9, 17)\n",
      "(nbs, nue): (9, 18)\n",
      "(nbs, nue): (9, 19)\n",
      "(nbs, nue): (10, 16)\n",
      "(nbs, nue): (10, 17)\n",
      "(nbs, nue): (10, 18)\n",
      "(nbs, nue): (10, 19)\n",
      "(nbs, nue): (11, 16)\n",
      "(nbs, nue): (11, 17)\n",
      "(nbs, nue): (11, 18)\n",
      "(nbs, nue): (11, 19)\n",
      "(nbs, nue): (12, 16)\n",
      "(nbs, nue): (12, 17)\n",
      "(nbs, nue): (12, 18)\n",
      "(nbs, nue): (12, 19)\n",
      "(nbs, nue): (13, 16)\n",
      "(nbs, nue): (13, 17)\n",
      "(nbs, nue): (13, 18)\n",
      "(nbs, nue): (13, 19)\n",
      "(nbs, nue): (14, 16)\n",
      "(nbs, nue): (14, 17)\n",
      "(nbs, nue): (14, 18)\n",
      "(nbs, nue): (14, 19)\n",
      "(nbs, nue): (15, 16)\n",
      "(nbs, nue): (15, 17)\n",
      "(nbs, nue): (15, 18)\n",
      "(nbs, nue): (15, 19)\n",
      "(nbs, nue): (16, 16)\n",
      "(nbs, nue): (16, 17)\n",
      "(nbs, nue): (16, 18)\n",
      "(nbs, nue): (16, 19)\n",
      "(nbs, nue): (17, 16)\n",
      "(nbs, nue): (17, 17)\n",
      "(nbs, nue): (17, 18)\n",
      "(nbs, nue): (17, 19)\n",
      "(nbs, nue): (18, 16)\n",
      "(nbs, nue): (18, 17)\n",
      "(nbs, nue): (18, 18)\n",
      "(nbs, nue): (18, 19)\n",
      "(nbs, nue): (19, 16)\n",
      "(nbs, nue): (19, 17)\n",
      "(nbs, nue): (19, 18)\n",
      "(nbs, nue): (19, 19)\n",
      "(nbs, nue): (20, 16)\n",
      "(nbs, nue): (20, 17)\n",
      "(nbs, nue): (20, 18)\n",
      "(nbs, nue): (20, 19)\n",
      "***********  [4]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [5]-Passed Through Channel *********** \n",
      "***********  [5]-Noise Added *********** \n",
      "***********  [5]-Resource Grid Extracted *********** \n",
      "UE-Index: 20 | slot-Index: 5\n",
      "UE-Index: 21 | slot-Index: 5\n",
      "UE-Index: 22 | slot-Index: 5\n",
      "UE-Index: 23 | slot-Index: 5\n",
      "***********  [5]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 20)\n",
      "(nbs, nue): (0, 21)\n",
      "(nbs, nue): (0, 22)\n",
      "(nbs, nue): (0, 23)\n",
      "(nbs, nue): (1, 20)\n",
      "(nbs, nue): (1, 21)\n",
      "(nbs, nue): (1, 22)\n",
      "(nbs, nue): (1, 23)\n",
      "(nbs, nue): (2, 20)\n",
      "(nbs, nue): (2, 21)\n",
      "(nbs, nue): (2, 22)\n",
      "(nbs, nue): (2, 23)\n",
      "(nbs, nue): (3, 20)\n",
      "(nbs, nue): (3, 21)\n",
      "(nbs, nue): (3, 22)\n",
      "(nbs, nue): (3, 23)\n",
      "(nbs, nue): (4, 20)\n",
      "(nbs, nue): (4, 21)\n",
      "(nbs, nue): (4, 22)\n",
      "(nbs, nue): (4, 23)\n",
      "(nbs, nue): (5, 20)\n",
      "(nbs, nue): (5, 21)\n",
      "(nbs, nue): (5, 22)\n",
      "(nbs, nue): (5, 23)\n",
      "(nbs, nue): (6, 20)\n",
      "(nbs, nue): (6, 21)\n",
      "(nbs, nue): (6, 22)\n",
      "(nbs, nue): (6, 23)\n",
      "(nbs, nue): (7, 20)\n",
      "(nbs, nue): (7, 21)\n",
      "(nbs, nue): (7, 22)\n",
      "(nbs, nue): (7, 23)\n",
      "(nbs, nue): (8, 20)\n",
      "(nbs, nue): (8, 21)\n",
      "(nbs, nue): (8, 22)\n",
      "(nbs, nue): (8, 23)\n",
      "(nbs, nue): (9, 20)\n",
      "(nbs, nue): (9, 21)\n",
      "(nbs, nue): (9, 22)\n",
      "(nbs, nue): (9, 23)\n",
      "(nbs, nue): (10, 20)\n",
      "(nbs, nue): (10, 21)\n",
      "(nbs, nue): (10, 22)\n",
      "(nbs, nue): (10, 23)\n",
      "(nbs, nue): (11, 20)\n",
      "(nbs, nue): (11, 21)\n",
      "(nbs, nue): (11, 22)\n",
      "(nbs, nue): (11, 23)\n",
      "(nbs, nue): (12, 20)\n",
      "(nbs, nue): (12, 21)\n",
      "(nbs, nue): (12, 22)\n",
      "(nbs, nue): (12, 23)\n",
      "(nbs, nue): (13, 20)\n",
      "(nbs, nue): (13, 21)\n",
      "(nbs, nue): (13, 22)\n",
      "(nbs, nue): (13, 23)\n",
      "(nbs, nue): (14, 20)\n",
      "(nbs, nue): (14, 21)\n",
      "(nbs, nue): (14, 22)\n",
      "(nbs, nue): (14, 23)\n",
      "(nbs, nue): (15, 20)\n",
      "(nbs, nue): (15, 21)\n",
      "(nbs, nue): (15, 22)\n",
      "(nbs, nue): (15, 23)\n",
      "(nbs, nue): (16, 20)\n",
      "(nbs, nue): (16, 21)\n",
      "(nbs, nue): (16, 22)\n",
      "(nbs, nue): (16, 23)\n",
      "(nbs, nue): (17, 20)\n",
      "(nbs, nue): (17, 21)\n",
      "(nbs, nue): (17, 22)\n",
      "(nbs, nue): (17, 23)\n",
      "(nbs, nue): (18, 20)\n",
      "(nbs, nue): (18, 21)\n",
      "(nbs, nue): (18, 22)\n",
      "(nbs, nue): (18, 23)\n",
      "(nbs, nue): (19, 20)\n",
      "(nbs, nue): (19, 21)\n",
      "(nbs, nue): (19, 22)\n",
      "(nbs, nue): (19, 23)\n",
      "(nbs, nue): (20, 20)\n",
      "(nbs, nue): (20, 21)\n",
      "(nbs, nue): (20, 22)\n",
      "(nbs, nue): (20, 23)\n",
      "***********  [5]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [6]-Passed Through Channel *********** \n",
      "***********  [6]-Noise Added *********** \n",
      "***********  [6]-Resource Grid Extracted *********** \n",
      "UE-Index: 24 | slot-Index: 6\n",
      "UE-Index: 25 | slot-Index: 6\n",
      "UE-Index: 26 | slot-Index: 6\n",
      "UE-Index: 27 | slot-Index: 6\n",
      "***********  [6]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 24)\n",
      "(nbs, nue): (0, 25)\n",
      "(nbs, nue): (0, 26)\n",
      "(nbs, nue): (0, 27)\n",
      "(nbs, nue): (1, 24)\n",
      "(nbs, nue): (1, 25)\n",
      "(nbs, nue): (1, 26)\n",
      "(nbs, nue): (1, 27)\n",
      "(nbs, nue): (2, 24)\n",
      "(nbs, nue): (2, 25)\n",
      "(nbs, nue): (2, 26)\n",
      "(nbs, nue): (2, 27)\n",
      "(nbs, nue): (3, 24)\n",
      "(nbs, nue): (3, 25)\n",
      "(nbs, nue): (3, 26)\n",
      "(nbs, nue): (3, 27)\n",
      "(nbs, nue): (4, 24)\n",
      "(nbs, nue): (4, 25)\n",
      "(nbs, nue): (4, 26)\n",
      "(nbs, nue): (4, 27)\n",
      "(nbs, nue): (5, 24)\n",
      "(nbs, nue): (5, 25)\n",
      "(nbs, nue): (5, 26)\n",
      "(nbs, nue): (5, 27)\n",
      "(nbs, nue): (6, 24)\n",
      "(nbs, nue): (6, 25)\n",
      "(nbs, nue): (6, 26)\n",
      "(nbs, nue): (6, 27)\n",
      "(nbs, nue): (7, 24)\n",
      "(nbs, nue): (7, 25)\n",
      "(nbs, nue): (7, 26)\n",
      "(nbs, nue): (7, 27)\n",
      "(nbs, nue): (8, 24)\n",
      "(nbs, nue): (8, 25)\n",
      "(nbs, nue): (8, 26)\n",
      "(nbs, nue): (8, 27)\n",
      "(nbs, nue): (9, 24)\n",
      "(nbs, nue): (9, 25)\n",
      "(nbs, nue): (9, 26)\n",
      "(nbs, nue): (9, 27)\n",
      "(nbs, nue): (10, 24)\n",
      "(nbs, nue): (10, 25)\n",
      "(nbs, nue): (10, 26)\n",
      "(nbs, nue): (10, 27)\n",
      "(nbs, nue): (11, 24)\n",
      "(nbs, nue): (11, 25)\n",
      "(nbs, nue): (11, 26)\n",
      "(nbs, nue): (11, 27)\n",
      "(nbs, nue): (12, 24)\n",
      "(nbs, nue): (12, 25)\n",
      "(nbs, nue): (12, 26)\n",
      "(nbs, nue): (12, 27)\n",
      "(nbs, nue): (13, 24)\n",
      "(nbs, nue): (13, 25)\n",
      "(nbs, nue): (13, 26)\n",
      "(nbs, nue): (13, 27)\n",
      "(nbs, nue): (14, 24)\n",
      "(nbs, nue): (14, 25)\n",
      "(nbs, nue): (14, 26)\n",
      "(nbs, nue): (14, 27)\n",
      "(nbs, nue): (15, 24)\n",
      "(nbs, nue): (15, 25)\n",
      "(nbs, nue): (15, 26)\n",
      "(nbs, nue): (15, 27)\n",
      "(nbs, nue): (16, 24)\n",
      "(nbs, nue): (16, 25)\n",
      "(nbs, nue): (16, 26)\n",
      "(nbs, nue): (16, 27)\n",
      "(nbs, nue): (17, 24)\n",
      "(nbs, nue): (17, 25)\n",
      "(nbs, nue): (17, 26)\n",
      "(nbs, nue): (17, 27)\n",
      "(nbs, nue): (18, 24)\n",
      "(nbs, nue): (18, 25)\n",
      "(nbs, nue): (18, 26)\n",
      "(nbs, nue): (18, 27)\n",
      "(nbs, nue): (19, 24)\n",
      "(nbs, nue): (19, 25)\n",
      "(nbs, nue): (19, 26)\n",
      "(nbs, nue): (19, 27)\n",
      "(nbs, nue): (20, 24)\n",
      "(nbs, nue): (20, 25)\n",
      "(nbs, nue): (20, 26)\n",
      "(nbs, nue): (20, 27)\n",
      "***********  [6]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [7]-Passed Through Channel *********** \n",
      "***********  [7]-Noise Added *********** \n",
      "***********  [7]-Resource Grid Extracted *********** \n",
      "UE-Index: 28 | slot-Index: 7\n",
      "UE-Index: 29 | slot-Index: 7\n",
      "UE-Index: 30 | slot-Index: 7\n",
      "UE-Index: 31 | slot-Index: 7\n",
      "***********  [7]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 28)\n",
      "(nbs, nue): (0, 29)\n",
      "(nbs, nue): (0, 30)\n",
      "(nbs, nue): (0, 31)\n",
      "(nbs, nue): (1, 28)\n",
      "(nbs, nue): (1, 29)\n",
      "(nbs, nue): (1, 30)\n",
      "(nbs, nue): (1, 31)\n",
      "(nbs, nue): (2, 28)\n",
      "(nbs, nue): (2, 29)\n",
      "(nbs, nue): (2, 30)\n",
      "(nbs, nue): (2, 31)\n",
      "(nbs, nue): (3, 28)\n",
      "(nbs, nue): (3, 29)\n",
      "(nbs, nue): (3, 30)\n",
      "(nbs, nue): (3, 31)\n",
      "(nbs, nue): (4, 28)\n",
      "(nbs, nue): (4, 29)\n",
      "(nbs, nue): (4, 30)\n",
      "(nbs, nue): (4, 31)\n",
      "(nbs, nue): (5, 28)\n",
      "(nbs, nue): (5, 29)\n",
      "(nbs, nue): (5, 30)\n",
      "(nbs, nue): (5, 31)\n",
      "(nbs, nue): (6, 28)\n",
      "(nbs, nue): (6, 29)\n",
      "(nbs, nue): (6, 30)\n",
      "(nbs, nue): (6, 31)\n",
      "(nbs, nue): (7, 28)\n",
      "(nbs, nue): (7, 29)\n",
      "(nbs, nue): (7, 30)\n",
      "(nbs, nue): (7, 31)\n",
      "(nbs, nue): (8, 28)\n",
      "(nbs, nue): (8, 29)\n",
      "(nbs, nue): (8, 30)\n",
      "(nbs, nue): (8, 31)\n",
      "(nbs, nue): (9, 28)\n",
      "(nbs, nue): (9, 29)\n",
      "(nbs, nue): (9, 30)\n",
      "(nbs, nue): (9, 31)\n",
      "(nbs, nue): (10, 28)\n",
      "(nbs, nue): (10, 29)\n",
      "(nbs, nue): (10, 30)\n",
      "(nbs, nue): (10, 31)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(nbs, nue): (11, 28)\n",
      "(nbs, nue): (11, 29)\n",
      "(nbs, nue): (11, 30)\n",
      "(nbs, nue): (11, 31)\n",
      "(nbs, nue): (12, 28)\n",
      "(nbs, nue): (12, 29)\n",
      "(nbs, nue): (12, 30)\n",
      "(nbs, nue): (12, 31)\n",
      "(nbs, nue): (13, 28)\n",
      "(nbs, nue): (13, 29)\n",
      "(nbs, nue): (13, 30)\n",
      "(nbs, nue): (13, 31)\n",
      "(nbs, nue): (14, 28)\n",
      "(nbs, nue): (14, 29)\n",
      "(nbs, nue): (14, 30)\n",
      "(nbs, nue): (14, 31)\n",
      "(nbs, nue): (15, 28)\n",
      "(nbs, nue): (15, 29)\n",
      "(nbs, nue): (15, 30)\n",
      "(nbs, nue): (15, 31)\n",
      "(nbs, nue): (16, 28)\n",
      "(nbs, nue): (16, 29)\n",
      "(nbs, nue): (16, 30)\n",
      "(nbs, nue): (16, 31)\n",
      "(nbs, nue): (17, 28)\n",
      "(nbs, nue): (17, 29)\n",
      "(nbs, nue): (17, 30)\n",
      "(nbs, nue): (17, 31)\n",
      "(nbs, nue): (18, 28)\n",
      "(nbs, nue): (18, 29)\n",
      "(nbs, nue): (18, 30)\n",
      "(nbs, nue): (18, 31)\n",
      "(nbs, nue): (19, 28)\n",
      "(nbs, nue): (19, 29)\n",
      "(nbs, nue): (19, 30)\n",
      "(nbs, nue): (19, 31)\n",
      "(nbs, nue): (20, 28)\n",
      "(nbs, nue): (20, 29)\n",
      "(nbs, nue): (20, 30)\n",
      "(nbs, nue): (20, 31)\n",
      "***********  [7]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [8]-Passed Through Channel *********** \n",
      "***********  [8]-Noise Added *********** \n",
      "***********  [8]-Resource Grid Extracted *********** \n",
      "UE-Index: 32 | slot-Index: 8\n",
      "UE-Index: 33 | slot-Index: 8\n",
      "UE-Index: 34 | slot-Index: 8\n",
      "UE-Index: 35 | slot-Index: 8\n",
      "***********  [8]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 32)\n",
      "(nbs, nue): (0, 33)\n",
      "(nbs, nue): (0, 34)\n",
      "(nbs, nue): (0, 35)\n",
      "(nbs, nue): (1, 32)\n",
      "(nbs, nue): (1, 33)\n",
      "(nbs, nue): (1, 34)\n",
      "(nbs, nue): (1, 35)\n",
      "(nbs, nue): (2, 32)\n",
      "(nbs, nue): (2, 33)\n",
      "(nbs, nue): (2, 34)\n",
      "(nbs, nue): (2, 35)\n",
      "(nbs, nue): (3, 32)\n",
      "(nbs, nue): (3, 33)\n",
      "(nbs, nue): (3, 34)\n",
      "(nbs, nue): (3, 35)\n",
      "(nbs, nue): (4, 32)\n",
      "(nbs, nue): (4, 33)\n",
      "(nbs, nue): (4, 34)\n",
      "(nbs, nue): (4, 35)\n",
      "(nbs, nue): (5, 32)\n",
      "(nbs, nue): (5, 33)\n",
      "(nbs, nue): (5, 34)\n",
      "(nbs, nue): (5, 35)\n",
      "(nbs, nue): (6, 32)\n",
      "(nbs, nue): (6, 33)\n",
      "(nbs, nue): (6, 34)\n",
      "(nbs, nue): (6, 35)\n",
      "(nbs, nue): (7, 32)\n",
      "(nbs, nue): (7, 33)\n",
      "(nbs, nue): (7, 34)\n",
      "(nbs, nue): (7, 35)\n",
      "(nbs, nue): (8, 32)\n",
      "(nbs, nue): (8, 33)\n",
      "(nbs, nue): (8, 34)\n",
      "(nbs, nue): (8, 35)\n",
      "(nbs, nue): (9, 32)\n",
      "(nbs, nue): (9, 33)\n",
      "(nbs, nue): (9, 34)\n",
      "(nbs, nue): (9, 35)\n",
      "(nbs, nue): (10, 32)\n",
      "(nbs, nue): (10, 33)\n",
      "(nbs, nue): (10, 34)\n",
      "(nbs, nue): (10, 35)\n",
      "(nbs, nue): (11, 32)\n",
      "(nbs, nue): (11, 33)\n",
      "(nbs, nue): (11, 34)\n",
      "(nbs, nue): (11, 35)\n",
      "(nbs, nue): (12, 32)\n",
      "(nbs, nue): (12, 33)\n",
      "(nbs, nue): (12, 34)\n",
      "(nbs, nue): (12, 35)\n",
      "(nbs, nue): (13, 32)\n",
      "(nbs, nue): (13, 33)\n",
      "(nbs, nue): (13, 34)\n",
      "(nbs, nue): (13, 35)\n",
      "(nbs, nue): (14, 32)\n",
      "(nbs, nue): (14, 33)\n",
      "(nbs, nue): (14, 34)\n",
      "(nbs, nue): (14, 35)\n",
      "(nbs, nue): (15, 32)\n",
      "(nbs, nue): (15, 33)\n",
      "(nbs, nue): (15, 34)\n",
      "(nbs, nue): (15, 35)\n",
      "(nbs, nue): (16, 32)\n",
      "(nbs, nue): (16, 33)\n",
      "(nbs, nue): (16, 34)\n",
      "(nbs, nue): (16, 35)\n",
      "(nbs, nue): (17, 32)\n",
      "(nbs, nue): (17, 33)\n",
      "(nbs, nue): (17, 34)\n",
      "(nbs, nue): (17, 35)\n",
      "(nbs, nue): (18, 32)\n",
      "(nbs, nue): (18, 33)\n",
      "(nbs, nue): (18, 34)\n",
      "(nbs, nue): (18, 35)\n",
      "(nbs, nue): (19, 32)\n",
      "(nbs, nue): (19, 33)\n",
      "(nbs, nue): (19, 34)\n",
      "(nbs, nue): (19, 35)\n",
      "(nbs, nue): (20, 32)\n",
      "(nbs, nue): (20, 33)\n",
      "(nbs, nue): (20, 34)\n",
      "(nbs, nue): (20, 35)\n",
      "***********  [8]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [9]-Passed Through Channel *********** \n",
      "***********  [9]-Noise Added *********** \n",
      "***********  [9]-Resource Grid Extracted *********** \n",
      "UE-Index: 36 | slot-Index: 9\n",
      "UE-Index: 37 | slot-Index: 9\n",
      "UE-Index: 38 | slot-Index: 9\n",
      "UE-Index: 39 | slot-Index: 9\n",
      "***********  [9]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 36)\n",
      "(nbs, nue): (0, 37)\n",
      "(nbs, nue): (0, 38)\n",
      "(nbs, nue): (0, 39)\n",
      "(nbs, nue): (1, 36)\n",
      "(nbs, nue): (1, 37)\n",
      "(nbs, nue): (1, 38)\n",
      "(nbs, nue): (1, 39)\n",
      "(nbs, nue): (2, 36)\n",
      "(nbs, nue): (2, 37)\n",
      "(nbs, nue): (2, 38)\n",
      "(nbs, nue): (2, 39)\n",
      "(nbs, nue): (3, 36)\n",
      "(nbs, nue): (3, 37)\n",
      "(nbs, nue): (3, 38)\n",
      "(nbs, nue): (3, 39)\n",
      "(nbs, nue): (4, 36)\n",
      "(nbs, nue): (4, 37)\n",
      "(nbs, nue): (4, 38)\n",
      "(nbs, nue): (4, 39)\n",
      "(nbs, nue): (5, 36)\n",
      "(nbs, nue): (5, 37)\n",
      "(nbs, nue): (5, 38)\n",
      "(nbs, nue): (5, 39)\n",
      "(nbs, nue): (6, 36)\n",
      "(nbs, nue): (6, 37)\n",
      "(nbs, nue): (6, 38)\n",
      "(nbs, nue): (6, 39)\n",
      "(nbs, nue): (7, 36)\n",
      "(nbs, nue): (7, 37)\n",
      "(nbs, nue): (7, 38)\n",
      "(nbs, nue): (7, 39)\n",
      "(nbs, nue): (8, 36)\n",
      "(nbs, nue): (8, 37)\n",
      "(nbs, nue): (8, 38)\n",
      "(nbs, nue): (8, 39)\n",
      "(nbs, nue): (9, 36)\n",
      "(nbs, nue): (9, 37)\n",
      "(nbs, nue): (9, 38)\n",
      "(nbs, nue): (9, 39)\n",
      "(nbs, nue): (10, 36)\n",
      "(nbs, nue): (10, 37)\n",
      "(nbs, nue): (10, 38)\n",
      "(nbs, nue): (10, 39)\n",
      "(nbs, nue): (11, 36)\n",
      "(nbs, nue): (11, 37)\n",
      "(nbs, nue): (11, 38)\n",
      "(nbs, nue): (11, 39)\n",
      "(nbs, nue): (12, 36)\n",
      "(nbs, nue): (12, 37)\n",
      "(nbs, nue): (12, 38)\n",
      "(nbs, nue): (12, 39)\n",
      "(nbs, nue): (13, 36)\n",
      "(nbs, nue): (13, 37)\n",
      "(nbs, nue): (13, 38)\n",
      "(nbs, nue): (13, 39)\n",
      "(nbs, nue): (14, 36)\n",
      "(nbs, nue): (14, 37)\n",
      "(nbs, nue): (14, 38)\n",
      "(nbs, nue): (14, 39)\n",
      "(nbs, nue): (15, 36)\n",
      "(nbs, nue): (15, 37)\n",
      "(nbs, nue): (15, 38)\n",
      "(nbs, nue): (15, 39)\n",
      "(nbs, nue): (16, 36)\n",
      "(nbs, nue): (16, 37)\n",
      "(nbs, nue): (16, 38)\n",
      "(nbs, nue): (16, 39)\n",
      "(nbs, nue): (17, 36)\n",
      "(nbs, nue): (17, 37)\n",
      "(nbs, nue): (17, 38)\n",
      "(nbs, nue): (17, 39)\n",
      "(nbs, nue): (18, 36)\n",
      "(nbs, nue): (18, 37)\n",
      "(nbs, nue): (18, 38)\n",
      "(nbs, nue): (18, 39)\n",
      "(nbs, nue): (19, 36)\n",
      "(nbs, nue): (19, 37)\n",
      "(nbs, nue): (19, 38)\n",
      "(nbs, nue): (19, 39)\n",
      "(nbs, nue): (20, 36)\n",
      "(nbs, nue): (20, 37)\n",
      "(nbs, nue): (20, 38)\n",
      "(nbs, nue): (20, 39)\n",
      "***********  [9]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [10]-Passed Through Channel *********** \n",
      "***********  [10]-Noise Added *********** \n",
      "***********  [10]-Resource Grid Extracted *********** \n",
      "UE-Index: 40 | slot-Index: 10\n",
      "UE-Index: 41 | slot-Index: 10\n",
      "UE-Index: 42 | slot-Index: 10\n",
      "UE-Index: 43 | slot-Index: 10\n",
      "***********  [10]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 40)\n",
      "(nbs, nue): (0, 41)\n",
      "(nbs, nue): (0, 42)\n",
      "(nbs, nue): (0, 43)\n",
      "(nbs, nue): (1, 40)\n",
      "(nbs, nue): (1, 41)\n",
      "(nbs, nue): (1, 42)\n",
      "(nbs, nue): (1, 43)\n",
      "(nbs, nue): (2, 40)\n",
      "(nbs, nue): (2, 41)\n",
      "(nbs, nue): (2, 42)\n",
      "(nbs, nue): (2, 43)\n",
      "(nbs, nue): (3, 40)\n",
      "(nbs, nue): (3, 41)\n",
      "(nbs, nue): (3, 42)\n",
      "(nbs, nue): (3, 43)\n",
      "(nbs, nue): (4, 40)\n",
      "(nbs, nue): (4, 41)\n",
      "(nbs, nue): (4, 42)\n",
      "(nbs, nue): (4, 43)\n",
      "(nbs, nue): (5, 40)\n",
      "(nbs, nue): (5, 41)\n",
      "(nbs, nue): (5, 42)\n",
      "(nbs, nue): (5, 43)\n",
      "(nbs, nue): (6, 40)\n",
      "(nbs, nue): (6, 41)\n",
      "(nbs, nue): (6, 42)\n",
      "(nbs, nue): (6, 43)\n",
      "(nbs, nue): (7, 40)\n",
      "(nbs, nue): (7, 41)\n",
      "(nbs, nue): (7, 42)\n",
      "(nbs, nue): (7, 43)\n",
      "(nbs, nue): (8, 40)\n",
      "(nbs, nue): (8, 41)\n",
      "(nbs, nue): (8, 42)\n",
      "(nbs, nue): (8, 43)\n",
      "(nbs, nue): (9, 40)\n",
      "(nbs, nue): (9, 41)\n",
      "(nbs, nue): (9, 42)\n",
      "(nbs, nue): (9, 43)\n",
      "(nbs, nue): (10, 40)\n",
      "(nbs, nue): (10, 41)\n",
      "(nbs, nue): (10, 42)\n",
      "(nbs, nue): (10, 43)\n",
      "(nbs, nue): (11, 40)\n",
      "(nbs, nue): (11, 41)\n",
      "(nbs, nue): (11, 42)\n",
      "(nbs, nue): (11, 43)\n",
      "(nbs, nue): (12, 40)\n",
      "(nbs, nue): (12, 41)\n",
      "(nbs, nue): (12, 42)\n",
      "(nbs, nue): (12, 43)\n",
      "(nbs, nue): (13, 40)\n",
      "(nbs, nue): (13, 41)\n",
      "(nbs, nue): (13, 42)\n",
      "(nbs, nue): (13, 43)\n",
      "(nbs, nue): (14, 40)\n",
      "(nbs, nue): (14, 41)\n",
      "(nbs, nue): (14, 42)\n",
      "(nbs, nue): (14, 43)\n",
      "(nbs, nue): (15, 40)\n",
      "(nbs, nue): (15, 41)\n",
      "(nbs, nue): (15, 42)\n",
      "(nbs, nue): (15, 43)\n",
      "(nbs, nue): (16, 40)\n",
      "(nbs, nue): (16, 41)\n",
      "(nbs, nue): (16, 42)\n",
      "(nbs, nue): (16, 43)\n",
      "(nbs, nue): (17, 40)\n",
      "(nbs, nue): (17, 41)\n",
      "(nbs, nue): (17, 42)\n",
      "(nbs, nue): (17, 43)\n",
      "(nbs, nue): (18, 40)\n",
      "(nbs, nue): (18, 41)\n",
      "(nbs, nue): (18, 42)\n",
      "(nbs, nue): (18, 43)\n",
      "(nbs, nue): (19, 40)\n",
      "(nbs, nue): (19, 41)\n",
      "(nbs, nue): (19, 42)\n",
      "(nbs, nue): (19, 43)\n",
      "(nbs, nue): (20, 40)\n",
      "(nbs, nue): (20, 41)\n",
      "(nbs, nue): (20, 42)\n",
      "(nbs, nue): (20, 43)\n",
      "***********  [10]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [11]-Passed Through Channel *********** \n",
      "***********  [11]-Noise Added *********** \n",
      "***********  [11]-Resource Grid Extracted *********** \n",
      "UE-Index: 44 | slot-Index: 11\n",
      "UE-Index: 45 | slot-Index: 11\n",
      "UE-Index: 46 | slot-Index: 11\n",
      "UE-Index: 47 | slot-Index: 11\n",
      "***********  [11]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 44)\n",
      "(nbs, nue): (0, 45)\n",
      "(nbs, nue): (0, 46)\n",
      "(nbs, nue): (0, 47)\n",
      "(nbs, nue): (1, 44)\n",
      "(nbs, nue): (1, 45)\n",
      "(nbs, nue): (1, 46)\n",
      "(nbs, nue): (1, 47)\n",
      "(nbs, nue): (2, 44)\n",
      "(nbs, nue): (2, 45)\n",
      "(nbs, nue): (2, 46)\n",
      "(nbs, nue): (2, 47)\n",
      "(nbs, nue): (3, 44)\n",
      "(nbs, nue): (3, 45)\n",
      "(nbs, nue): (3, 46)\n",
      "(nbs, nue): (3, 47)\n",
      "(nbs, nue): (4, 44)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(nbs, nue): (4, 45)\n",
      "(nbs, nue): (4, 46)\n",
      "(nbs, nue): (4, 47)\n",
      "(nbs, nue): (5, 44)\n",
      "(nbs, nue): (5, 45)\n",
      "(nbs, nue): (5, 46)\n",
      "(nbs, nue): (5, 47)\n",
      "(nbs, nue): (6, 44)\n",
      "(nbs, nue): (6, 45)\n",
      "(nbs, nue): (6, 46)\n",
      "(nbs, nue): (6, 47)\n",
      "(nbs, nue): (7, 44)\n",
      "(nbs, nue): (7, 45)\n",
      "(nbs, nue): (7, 46)\n",
      "(nbs, nue): (7, 47)\n",
      "(nbs, nue): (8, 44)\n",
      "(nbs, nue): (8, 45)\n",
      "(nbs, nue): (8, 46)\n",
      "(nbs, nue): (8, 47)\n",
      "(nbs, nue): (9, 44)\n",
      "(nbs, nue): (9, 45)\n",
      "(nbs, nue): (9, 46)\n",
      "(nbs, nue): (9, 47)\n",
      "(nbs, nue): (10, 44)\n",
      "(nbs, nue): (10, 45)\n",
      "(nbs, nue): (10, 46)\n",
      "(nbs, nue): (10, 47)\n",
      "(nbs, nue): (11, 44)\n",
      "(nbs, nue): (11, 45)\n",
      "(nbs, nue): (11, 46)\n",
      "(nbs, nue): (11, 47)\n",
      "(nbs, nue): (12, 44)\n",
      "(nbs, nue): (12, 45)\n",
      "(nbs, nue): (12, 46)\n",
      "(nbs, nue): (12, 47)\n",
      "(nbs, nue): (13, 44)\n",
      "(nbs, nue): (13, 45)\n",
      "(nbs, nue): (13, 46)\n",
      "(nbs, nue): (13, 47)\n",
      "(nbs, nue): (14, 44)\n",
      "(nbs, nue): (14, 45)\n",
      "(nbs, nue): (14, 46)\n",
      "(nbs, nue): (14, 47)\n",
      "(nbs, nue): (15, 44)\n",
      "(nbs, nue): (15, 45)\n",
      "(nbs, nue): (15, 46)\n",
      "(nbs, nue): (15, 47)\n",
      "(nbs, nue): (16, 44)\n",
      "(nbs, nue): (16, 45)\n",
      "(nbs, nue): (16, 46)\n",
      "(nbs, nue): (16, 47)\n",
      "(nbs, nue): (17, 44)\n",
      "(nbs, nue): (17, 45)\n",
      "(nbs, nue): (17, 46)\n",
      "(nbs, nue): (17, 47)\n",
      "(nbs, nue): (18, 44)\n",
      "(nbs, nue): (18, 45)\n",
      "(nbs, nue): (18, 46)\n",
      "(nbs, nue): (18, 47)\n",
      "(nbs, nue): (19, 44)\n",
      "(nbs, nue): (19, 45)\n",
      "(nbs, nue): (19, 46)\n",
      "(nbs, nue): (19, 47)\n",
      "(nbs, nue): (20, 44)\n",
      "(nbs, nue): (20, 45)\n",
      "(nbs, nue): (20, 46)\n",
      "(nbs, nue): (20, 47)\n",
      "***********  [11]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [12]-Passed Through Channel *********** \n",
      "***********  [12]-Noise Added *********** \n",
      "***********  [12]-Resource Grid Extracted *********** \n",
      "UE-Index: 48 | slot-Index: 12\n",
      "UE-Index: 49 | slot-Index: 12\n",
      "UE-Index: 50 | slot-Index: 12\n",
      "UE-Index: 51 | slot-Index: 12\n",
      "***********  [12]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 48)\n",
      "(nbs, nue): (0, 49)\n",
      "(nbs, nue): (0, 50)\n",
      "(nbs, nue): (0, 51)\n",
      "(nbs, nue): (1, 48)\n",
      "(nbs, nue): (1, 49)\n",
      "(nbs, nue): (1, 50)\n",
      "(nbs, nue): (1, 51)\n",
      "(nbs, nue): (2, 48)\n",
      "(nbs, nue): (2, 49)\n",
      "(nbs, nue): (2, 50)\n",
      "(nbs, nue): (2, 51)\n",
      "(nbs, nue): (3, 48)\n",
      "(nbs, nue): (3, 49)\n",
      "(nbs, nue): (3, 50)\n",
      "(nbs, nue): (3, 51)\n",
      "(nbs, nue): (4, 48)\n",
      "(nbs, nue): (4, 49)\n",
      "(nbs, nue): (4, 50)\n",
      "(nbs, nue): (4, 51)\n",
      "(nbs, nue): (5, 48)\n",
      "(nbs, nue): (5, 49)\n",
      "(nbs, nue): (5, 50)\n",
      "(nbs, nue): (5, 51)\n",
      "(nbs, nue): (6, 48)\n",
      "(nbs, nue): (6, 49)\n",
      "(nbs, nue): (6, 50)\n",
      "(nbs, nue): (6, 51)\n",
      "(nbs, nue): (7, 48)\n",
      "(nbs, nue): (7, 49)\n",
      "(nbs, nue): (7, 50)\n",
      "(nbs, nue): (7, 51)\n",
      "(nbs, nue): (8, 48)\n",
      "(nbs, nue): (8, 49)\n",
      "(nbs, nue): (8, 50)\n",
      "(nbs, nue): (8, 51)\n",
      "(nbs, nue): (9, 48)\n",
      "(nbs, nue): (9, 49)\n",
      "(nbs, nue): (9, 50)\n",
      "(nbs, nue): (9, 51)\n",
      "(nbs, nue): (10, 48)\n",
      "(nbs, nue): (10, 49)\n",
      "(nbs, nue): (10, 50)\n",
      "(nbs, nue): (10, 51)\n",
      "(nbs, nue): (11, 48)\n",
      "(nbs, nue): (11, 49)\n",
      "(nbs, nue): (11, 50)\n",
      "(nbs, nue): (11, 51)\n",
      "(nbs, nue): (12, 48)\n",
      "(nbs, nue): (12, 49)\n",
      "(nbs, nue): (12, 50)\n",
      "(nbs, nue): (12, 51)\n",
      "(nbs, nue): (13, 48)\n",
      "(nbs, nue): (13, 49)\n",
      "(nbs, nue): (13, 50)\n",
      "(nbs, nue): (13, 51)\n",
      "(nbs, nue): (14, 48)\n",
      "(nbs, nue): (14, 49)\n",
      "(nbs, nue): (14, 50)\n",
      "(nbs, nue): (14, 51)\n",
      "(nbs, nue): (15, 48)\n",
      "(nbs, nue): (15, 49)\n",
      "(nbs, nue): (15, 50)\n",
      "(nbs, nue): (15, 51)\n",
      "(nbs, nue): (16, 48)\n",
      "(nbs, nue): (16, 49)\n",
      "(nbs, nue): (16, 50)\n",
      "(nbs, nue): (16, 51)\n",
      "(nbs, nue): (17, 48)\n",
      "(nbs, nue): (17, 49)\n",
      "(nbs, nue): (17, 50)\n",
      "(nbs, nue): (17, 51)\n",
      "(nbs, nue): (18, 48)\n",
      "(nbs, nue): (18, 49)\n",
      "(nbs, nue): (18, 50)\n",
      "(nbs, nue): (18, 51)\n",
      "(nbs, nue): (19, 48)\n",
      "(nbs, nue): (19, 49)\n",
      "(nbs, nue): (19, 50)\n",
      "(nbs, nue): (19, 51)\n",
      "(nbs, nue): (20, 48)\n",
      "(nbs, nue): (20, 49)\n",
      "(nbs, nue): (20, 50)\n",
      "(nbs, nue): (20, 51)\n",
      "***********  [12]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [13]-Passed Through Channel *********** \n",
      "***********  [13]-Noise Added *********** \n",
      "***********  [13]-Resource Grid Extracted *********** \n",
      "UE-Index: 52 | slot-Index: 13\n",
      "UE-Index: 53 | slot-Index: 13\n",
      "UE-Index: 54 | slot-Index: 13\n",
      "UE-Index: 55 | slot-Index: 13\n",
      "***********  [13]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 52)\n",
      "(nbs, nue): (0, 53)\n",
      "(nbs, nue): (0, 54)\n",
      "(nbs, nue): (0, 55)\n",
      "(nbs, nue): (1, 52)\n",
      "(nbs, nue): (1, 53)\n",
      "(nbs, nue): (1, 54)\n",
      "(nbs, nue): (1, 55)\n",
      "(nbs, nue): (2, 52)\n",
      "(nbs, nue): (2, 53)\n",
      "(nbs, nue): (2, 54)\n",
      "(nbs, nue): (2, 55)\n",
      "(nbs, nue): (3, 52)\n",
      "(nbs, nue): (3, 53)\n",
      "(nbs, nue): (3, 54)\n",
      "(nbs, nue): (3, 55)\n",
      "(nbs, nue): (4, 52)\n",
      "(nbs, nue): (4, 53)\n",
      "(nbs, nue): (4, 54)\n",
      "(nbs, nue): (4, 55)\n",
      "(nbs, nue): (5, 52)\n",
      "(nbs, nue): (5, 53)\n",
      "(nbs, nue): (5, 54)\n",
      "(nbs, nue): (5, 55)\n",
      "(nbs, nue): (6, 52)\n",
      "(nbs, nue): (6, 53)\n",
      "(nbs, nue): (6, 54)\n",
      "(nbs, nue): (6, 55)\n",
      "(nbs, nue): (7, 52)\n",
      "(nbs, nue): (7, 53)\n",
      "(nbs, nue): (7, 54)\n",
      "(nbs, nue): (7, 55)\n",
      "(nbs, nue): (8, 52)\n",
      "(nbs, nue): (8, 53)\n",
      "(nbs, nue): (8, 54)\n",
      "(nbs, nue): (8, 55)\n",
      "(nbs, nue): (9, 52)\n",
      "(nbs, nue): (9, 53)\n",
      "(nbs, nue): (9, 54)\n",
      "(nbs, nue): (9, 55)\n",
      "(nbs, nue): (10, 52)\n",
      "(nbs, nue): (10, 53)\n",
      "(nbs, nue): (10, 54)\n",
      "(nbs, nue): (10, 55)\n",
      "(nbs, nue): (11, 52)\n",
      "(nbs, nue): (11, 53)\n",
      "(nbs, nue): (11, 54)\n",
      "(nbs, nue): (11, 55)\n",
      "(nbs, nue): (12, 52)\n",
      "(nbs, nue): (12, 53)\n",
      "(nbs, nue): (12, 54)\n",
      "(nbs, nue): (12, 55)\n",
      "(nbs, nue): (13, 52)\n",
      "(nbs, nue): (13, 53)\n",
      "(nbs, nue): (13, 54)\n",
      "(nbs, nue): (13, 55)\n",
      "(nbs, nue): (14, 52)\n",
      "(nbs, nue): (14, 53)\n",
      "(nbs, nue): (14, 54)\n",
      "(nbs, nue): (14, 55)\n",
      "(nbs, nue): (15, 52)\n",
      "(nbs, nue): (15, 53)\n",
      "(nbs, nue): (15, 54)\n",
      "(nbs, nue): (15, 55)\n",
      "(nbs, nue): (16, 52)\n",
      "(nbs, nue): (16, 53)\n",
      "(nbs, nue): (16, 54)\n",
      "(nbs, nue): (16, 55)\n",
      "(nbs, nue): (17, 52)\n",
      "(nbs, nue): (17, 53)\n",
      "(nbs, nue): (17, 54)\n",
      "(nbs, nue): (17, 55)\n",
      "(nbs, nue): (18, 52)\n",
      "(nbs, nue): (18, 53)\n",
      "(nbs, nue): (18, 54)\n",
      "(nbs, nue): (18, 55)\n",
      "(nbs, nue): (19, 52)\n",
      "(nbs, nue): (19, 53)\n",
      "(nbs, nue): (19, 54)\n",
      "(nbs, nue): (19, 55)\n",
      "(nbs, nue): (20, 52)\n",
      "(nbs, nue): (20, 53)\n",
      "(nbs, nue): (20, 54)\n",
      "(nbs, nue): (20, 55)\n",
      "***********  [13]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [14]-Passed Through Channel *********** \n",
      "***********  [14]-Noise Added *********** \n",
      "***********  [14]-Resource Grid Extracted *********** \n",
      "UE-Index: 56 | slot-Index: 14\n",
      "UE-Index: 57 | slot-Index: 14\n",
      "UE-Index: 58 | slot-Index: 14\n",
      "UE-Index: 59 | slot-Index: 14\n",
      "***********  [14]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 56)\n",
      "(nbs, nue): (0, 57)\n",
      "(nbs, nue): (0, 58)\n",
      "(nbs, nue): (0, 59)\n",
      "(nbs, nue): (1, 56)\n",
      "(nbs, nue): (1, 57)\n",
      "(nbs, nue): (1, 58)\n",
      "(nbs, nue): (1, 59)\n",
      "(nbs, nue): (2, 56)\n",
      "(nbs, nue): (2, 57)\n",
      "(nbs, nue): (2, 58)\n",
      "(nbs, nue): (2, 59)\n",
      "(nbs, nue): (3, 56)\n",
      "(nbs, nue): (3, 57)\n",
      "(nbs, nue): (3, 58)\n",
      "(nbs, nue): (3, 59)\n",
      "(nbs, nue): (4, 56)\n",
      "(nbs, nue): (4, 57)\n",
      "(nbs, nue): (4, 58)\n",
      "(nbs, nue): (4, 59)\n",
      "(nbs, nue): (5, 56)\n",
      "(nbs, nue): (5, 57)\n",
      "(nbs, nue): (5, 58)\n",
      "(nbs, nue): (5, 59)\n",
      "(nbs, nue): (6, 56)\n",
      "(nbs, nue): (6, 57)\n",
      "(nbs, nue): (6, 58)\n",
      "(nbs, nue): (6, 59)\n",
      "(nbs, nue): (7, 56)\n",
      "(nbs, nue): (7, 57)\n",
      "(nbs, nue): (7, 58)\n",
      "(nbs, nue): (7, 59)\n",
      "(nbs, nue): (8, 56)\n",
      "(nbs, nue): (8, 57)\n",
      "(nbs, nue): (8, 58)\n",
      "(nbs, nue): (8, 59)\n",
      "(nbs, nue): (9, 56)\n",
      "(nbs, nue): (9, 57)\n",
      "(nbs, nue): (9, 58)\n",
      "(nbs, nue): (9, 59)\n",
      "(nbs, nue): (10, 56)\n",
      "(nbs, nue): (10, 57)\n",
      "(nbs, nue): (10, 58)\n",
      "(nbs, nue): (10, 59)\n",
      "(nbs, nue): (11, 56)\n",
      "(nbs, nue): (11, 57)\n",
      "(nbs, nue): (11, 58)\n",
      "(nbs, nue): (11, 59)\n",
      "(nbs, nue): (12, 56)\n",
      "(nbs, nue): (12, 57)\n",
      "(nbs, nue): (12, 58)\n",
      "(nbs, nue): (12, 59)\n",
      "(nbs, nue): (13, 56)\n",
      "(nbs, nue): (13, 57)\n",
      "(nbs, nue): (13, 58)\n",
      "(nbs, nue): (13, 59)\n",
      "(nbs, nue): (14, 56)\n",
      "(nbs, nue): (14, 57)\n",
      "(nbs, nue): (14, 58)\n",
      "(nbs, nue): (14, 59)\n",
      "(nbs, nue): (15, 56)\n",
      "(nbs, nue): (15, 57)\n",
      "(nbs, nue): (15, 58)\n",
      "(nbs, nue): (15, 59)\n",
      "(nbs, nue): (16, 56)\n",
      "(nbs, nue): (16, 57)\n",
      "(nbs, nue): (16, 58)\n",
      "(nbs, nue): (16, 59)\n",
      "(nbs, nue): (17, 56)\n",
      "(nbs, nue): (17, 57)\n",
      "(nbs, nue): (17, 58)\n",
      "(nbs, nue): (17, 59)\n",
      "(nbs, nue): (18, 56)\n",
      "(nbs, nue): (18, 57)\n",
      "(nbs, nue): (18, 58)\n",
      "(nbs, nue): (18, 59)\n",
      "(nbs, nue): (19, 56)\n",
      "(nbs, nue): (19, 57)\n",
      "(nbs, nue): (19, 58)\n",
      "(nbs, nue): (19, 59)\n",
      "(nbs, nue): (20, 56)\n",
      "(nbs, nue): (20, 57)\n",
      "(nbs, nue): (20, 58)\n",
      "(nbs, nue): (20, 59)\n",
      "***********  [14]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [15]-Passed Through Channel *********** \n",
      "***********  [15]-Noise Added *********** \n",
      "***********  [15]-Resource Grid Extracted *********** \n",
      "UE-Index: 60 | slot-Index: 15\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UE-Index: 61 | slot-Index: 15\n",
      "UE-Index: 62 | slot-Index: 15\n",
      "UE-Index: 63 | slot-Index: 15\n",
      "***********  [15]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 60)\n",
      "(nbs, nue): (0, 61)\n",
      "(nbs, nue): (0, 62)\n",
      "(nbs, nue): (0, 63)\n",
      "(nbs, nue): (1, 60)\n",
      "(nbs, nue): (1, 61)\n",
      "(nbs, nue): (1, 62)\n",
      "(nbs, nue): (1, 63)\n",
      "(nbs, nue): (2, 60)\n",
      "(nbs, nue): (2, 61)\n",
      "(nbs, nue): (2, 62)\n",
      "(nbs, nue): (2, 63)\n",
      "(nbs, nue): (3, 60)\n",
      "(nbs, nue): (3, 61)\n",
      "(nbs, nue): (3, 62)\n",
      "(nbs, nue): (3, 63)\n",
      "(nbs, nue): (4, 60)\n",
      "(nbs, nue): (4, 61)\n",
      "(nbs, nue): (4, 62)\n",
      "(nbs, nue): (4, 63)\n",
      "(nbs, nue): (5, 60)\n",
      "(nbs, nue): (5, 61)\n",
      "(nbs, nue): (5, 62)\n",
      "(nbs, nue): (5, 63)\n",
      "(nbs, nue): (6, 60)\n",
      "(nbs, nue): (6, 61)\n",
      "(nbs, nue): (6, 62)\n",
      "(nbs, nue): (6, 63)\n",
      "(nbs, nue): (7, 60)\n",
      "(nbs, nue): (7, 61)\n",
      "(nbs, nue): (7, 62)\n",
      "(nbs, nue): (7, 63)\n",
      "(nbs, nue): (8, 60)\n",
      "(nbs, nue): (8, 61)\n",
      "(nbs, nue): (8, 62)\n",
      "(nbs, nue): (8, 63)\n",
      "(nbs, nue): (9, 60)\n",
      "(nbs, nue): (9, 61)\n",
      "(nbs, nue): (9, 62)\n",
      "(nbs, nue): (9, 63)\n",
      "(nbs, nue): (10, 60)\n",
      "(nbs, nue): (10, 61)\n",
      "(nbs, nue): (10, 62)\n",
      "(nbs, nue): (10, 63)\n",
      "(nbs, nue): (11, 60)\n",
      "(nbs, nue): (11, 61)\n",
      "(nbs, nue): (11, 62)\n",
      "(nbs, nue): (11, 63)\n",
      "(nbs, nue): (12, 60)\n",
      "(nbs, nue): (12, 61)\n",
      "(nbs, nue): (12, 62)\n",
      "(nbs, nue): (12, 63)\n",
      "(nbs, nue): (13, 60)\n",
      "(nbs, nue): (13, 61)\n",
      "(nbs, nue): (13, 62)\n",
      "(nbs, nue): (13, 63)\n",
      "(nbs, nue): (14, 60)\n",
      "(nbs, nue): (14, 61)\n",
      "(nbs, nue): (14, 62)\n",
      "(nbs, nue): (14, 63)\n",
      "(nbs, nue): (15, 60)\n",
      "(nbs, nue): (15, 61)\n",
      "(nbs, nue): (15, 62)\n",
      "(nbs, nue): (15, 63)\n",
      "(nbs, nue): (16, 60)\n",
      "(nbs, nue): (16, 61)\n",
      "(nbs, nue): (16, 62)\n",
      "(nbs, nue): (16, 63)\n",
      "(nbs, nue): (17, 60)\n",
      "(nbs, nue): (17, 61)\n",
      "(nbs, nue): (17, 62)\n",
      "(nbs, nue): (17, 63)\n",
      "(nbs, nue): (18, 60)\n",
      "(nbs, nue): (18, 61)\n",
      "(nbs, nue): (18, 62)\n",
      "(nbs, nue): (18, 63)\n",
      "(nbs, nue): (19, 60)\n",
      "(nbs, nue): (19, 61)\n",
      "(nbs, nue): (19, 62)\n",
      "(nbs, nue): (19, 63)\n",
      "(nbs, nue): (20, 60)\n",
      "(nbs, nue): (20, 61)\n",
      "(nbs, nue): (20, 62)\n",
      "(nbs, nue): (20, 63)\n",
      "***********  [15]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [16]-Passed Through Channel *********** \n",
      "***********  [16]-Noise Added *********** \n",
      "***********  [16]-Resource Grid Extracted *********** \n",
      "UE-Index: 64 | slot-Index: 16\n",
      "UE-Index: 65 | slot-Index: 16\n",
      "UE-Index: 66 | slot-Index: 16\n",
      "UE-Index: 67 | slot-Index: 16\n",
      "***********  [16]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 64)\n",
      "(nbs, nue): (0, 65)\n",
      "(nbs, nue): (0, 66)\n",
      "(nbs, nue): (0, 67)\n",
      "(nbs, nue): (1, 64)\n",
      "(nbs, nue): (1, 65)\n",
      "(nbs, nue): (1, 66)\n",
      "(nbs, nue): (1, 67)\n",
      "(nbs, nue): (2, 64)\n",
      "(nbs, nue): (2, 65)\n",
      "(nbs, nue): (2, 66)\n",
      "(nbs, nue): (2, 67)\n",
      "(nbs, nue): (3, 64)\n",
      "(nbs, nue): (3, 65)\n",
      "(nbs, nue): (3, 66)\n",
      "(nbs, nue): (3, 67)\n",
      "(nbs, nue): (4, 64)\n",
      "(nbs, nue): (4, 65)\n",
      "(nbs, nue): (4, 66)\n",
      "(nbs, nue): (4, 67)\n",
      "(nbs, nue): (5, 64)\n",
      "(nbs, nue): (5, 65)\n",
      "(nbs, nue): (5, 66)\n",
      "(nbs, nue): (5, 67)\n",
      "(nbs, nue): (6, 64)\n",
      "(nbs, nue): (6, 65)\n",
      "(nbs, nue): (6, 66)\n",
      "(nbs, nue): (6, 67)\n",
      "(nbs, nue): (7, 64)\n",
      "(nbs, nue): (7, 65)\n",
      "(nbs, nue): (7, 66)\n",
      "(nbs, nue): (7, 67)\n",
      "(nbs, nue): (8, 64)\n",
      "(nbs, nue): (8, 65)\n",
      "(nbs, nue): (8, 66)\n",
      "(nbs, nue): (8, 67)\n",
      "(nbs, nue): (9, 64)\n",
      "(nbs, nue): (9, 65)\n",
      "(nbs, nue): (9, 66)\n",
      "(nbs, nue): (9, 67)\n",
      "(nbs, nue): (10, 64)\n",
      "(nbs, nue): (10, 65)\n",
      "(nbs, nue): (10, 66)\n",
      "(nbs, nue): (10, 67)\n",
      "(nbs, nue): (11, 64)\n",
      "(nbs, nue): (11, 65)\n",
      "(nbs, nue): (11, 66)\n",
      "(nbs, nue): (11, 67)\n",
      "(nbs, nue): (12, 64)\n",
      "(nbs, nue): (12, 65)\n",
      "(nbs, nue): (12, 66)\n",
      "(nbs, nue): (12, 67)\n",
      "(nbs, nue): (13, 64)\n",
      "(nbs, nue): (13, 65)\n",
      "(nbs, nue): (13, 66)\n",
      "(nbs, nue): (13, 67)\n",
      "(nbs, nue): (14, 64)\n",
      "(nbs, nue): (14, 65)\n",
      "(nbs, nue): (14, 66)\n",
      "(nbs, nue): (14, 67)\n",
      "(nbs, nue): (15, 64)\n",
      "(nbs, nue): (15, 65)\n",
      "(nbs, nue): (15, 66)\n",
      "(nbs, nue): (15, 67)\n",
      "(nbs, nue): (16, 64)\n",
      "(nbs, nue): (16, 65)\n",
      "(nbs, nue): (16, 66)\n",
      "(nbs, nue): (16, 67)\n",
      "(nbs, nue): (17, 64)\n",
      "(nbs, nue): (17, 65)\n",
      "(nbs, nue): (17, 66)\n",
      "(nbs, nue): (17, 67)\n",
      "(nbs, nue): (18, 64)\n",
      "(nbs, nue): (18, 65)\n",
      "(nbs, nue): (18, 66)\n",
      "(nbs, nue): (18, 67)\n",
      "(nbs, nue): (19, 64)\n",
      "(nbs, nue): (19, 65)\n",
      "(nbs, nue): (19, 66)\n",
      "(nbs, nue): (19, 67)\n",
      "(nbs, nue): (20, 64)\n",
      "(nbs, nue): (20, 65)\n",
      "(nbs, nue): (20, 66)\n",
      "(nbs, nue): (20, 67)\n",
      "***********  [16]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [17]-Passed Through Channel *********** \n",
      "***********  [17]-Noise Added *********** \n",
      "***********  [17]-Resource Grid Extracted *********** \n",
      "UE-Index: 68 | slot-Index: 17\n",
      "UE-Index: 69 | slot-Index: 17\n",
      "UE-Index: 70 | slot-Index: 17\n",
      "UE-Index: 71 | slot-Index: 17\n",
      "***********  [17]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 68)\n",
      "(nbs, nue): (0, 69)\n",
      "(nbs, nue): (0, 70)\n",
      "(nbs, nue): (0, 71)\n",
      "(nbs, nue): (1, 68)\n",
      "(nbs, nue): (1, 69)\n",
      "(nbs, nue): (1, 70)\n",
      "(nbs, nue): (1, 71)\n",
      "(nbs, nue): (2, 68)\n",
      "(nbs, nue): (2, 69)\n",
      "(nbs, nue): (2, 70)\n",
      "(nbs, nue): (2, 71)\n",
      "(nbs, nue): (3, 68)\n",
      "(nbs, nue): (3, 69)\n",
      "(nbs, nue): (3, 70)\n",
      "(nbs, nue): (3, 71)\n",
      "(nbs, nue): (4, 68)\n",
      "(nbs, nue): (4, 69)\n",
      "(nbs, nue): (4, 70)\n",
      "(nbs, nue): (4, 71)\n",
      "(nbs, nue): (5, 68)\n",
      "(nbs, nue): (5, 69)\n",
      "(nbs, nue): (5, 70)\n",
      "(nbs, nue): (5, 71)\n",
      "(nbs, nue): (6, 68)\n",
      "(nbs, nue): (6, 69)\n",
      "(nbs, nue): (6, 70)\n",
      "(nbs, nue): (6, 71)\n",
      "(nbs, nue): (7, 68)\n",
      "(nbs, nue): (7, 69)\n",
      "(nbs, nue): (7, 70)\n",
      "(nbs, nue): (7, 71)\n",
      "(nbs, nue): (8, 68)\n",
      "(nbs, nue): (8, 69)\n",
      "(nbs, nue): (8, 70)\n",
      "(nbs, nue): (8, 71)\n",
      "(nbs, nue): (9, 68)\n",
      "(nbs, nue): (9, 69)\n",
      "(nbs, nue): (9, 70)\n",
      "(nbs, nue): (9, 71)\n",
      "(nbs, nue): (10, 68)\n",
      "(nbs, nue): (10, 69)\n",
      "(nbs, nue): (10, 70)\n",
      "(nbs, nue): (10, 71)\n",
      "(nbs, nue): (11, 68)\n",
      "(nbs, nue): (11, 69)\n",
      "(nbs, nue): (11, 70)\n",
      "(nbs, nue): (11, 71)\n",
      "(nbs, nue): (12, 68)\n",
      "(nbs, nue): (12, 69)\n",
      "(nbs, nue): (12, 70)\n",
      "(nbs, nue): (12, 71)\n",
      "(nbs, nue): (13, 68)\n",
      "(nbs, nue): (13, 69)\n",
      "(nbs, nue): (13, 70)\n",
      "(nbs, nue): (13, 71)\n",
      "(nbs, nue): (14, 68)\n",
      "(nbs, nue): (14, 69)\n",
      "(nbs, nue): (14, 70)\n",
      "(nbs, nue): (14, 71)\n",
      "(nbs, nue): (15, 68)\n",
      "(nbs, nue): (15, 69)\n",
      "(nbs, nue): (15, 70)\n",
      "(nbs, nue): (15, 71)\n",
      "(nbs, nue): (16, 68)\n",
      "(nbs, nue): (16, 69)\n",
      "(nbs, nue): (16, 70)\n",
      "(nbs, nue): (16, 71)\n",
      "(nbs, nue): (17, 68)\n",
      "(nbs, nue): (17, 69)\n",
      "(nbs, nue): (17, 70)\n",
      "(nbs, nue): (17, 71)\n",
      "(nbs, nue): (18, 68)\n",
      "(nbs, nue): (18, 69)\n",
      "(nbs, nue): (18, 70)\n",
      "(nbs, nue): (18, 71)\n",
      "(nbs, nue): (19, 68)\n",
      "(nbs, nue): (19, 69)\n",
      "(nbs, nue): (19, 70)\n",
      "(nbs, nue): (19, 71)\n",
      "(nbs, nue): (20, 68)\n",
      "(nbs, nue): (20, 69)\n",
      "(nbs, nue): (20, 70)\n",
      "(nbs, nue): (20, 71)\n",
      "***********  [17]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [18]-Passed Through Channel *********** \n",
      "***********  [18]-Noise Added *********** \n",
      "***********  [18]-Resource Grid Extracted *********** \n",
      "UE-Index: 72 | slot-Index: 18\n",
      "UE-Index: 73 | slot-Index: 18\n",
      "UE-Index: 74 | slot-Index: 18\n",
      "UE-Index: 75 | slot-Index: 18\n",
      "***********  [18]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 72)\n",
      "(nbs, nue): (0, 73)\n",
      "(nbs, nue): (0, 74)\n",
      "(nbs, nue): (0, 75)\n",
      "(nbs, nue): (1, 72)\n",
      "(nbs, nue): (1, 73)\n",
      "(nbs, nue): (1, 74)\n",
      "(nbs, nue): (1, 75)\n",
      "(nbs, nue): (2, 72)\n",
      "(nbs, nue): (2, 73)\n",
      "(nbs, nue): (2, 74)\n",
      "(nbs, nue): (2, 75)\n",
      "(nbs, nue): (3, 72)\n",
      "(nbs, nue): (3, 73)\n",
      "(nbs, nue): (3, 74)\n",
      "(nbs, nue): (3, 75)\n",
      "(nbs, nue): (4, 72)\n",
      "(nbs, nue): (4, 73)\n",
      "(nbs, nue): (4, 74)\n",
      "(nbs, nue): (4, 75)\n",
      "(nbs, nue): (5, 72)\n",
      "(nbs, nue): (5, 73)\n",
      "(nbs, nue): (5, 74)\n",
      "(nbs, nue): (5, 75)\n",
      "(nbs, nue): (6, 72)\n",
      "(nbs, nue): (6, 73)\n",
      "(nbs, nue): (6, 74)\n",
      "(nbs, nue): (6, 75)\n",
      "(nbs, nue): (7, 72)\n",
      "(nbs, nue): (7, 73)\n",
      "(nbs, nue): (7, 74)\n",
      "(nbs, nue): (7, 75)\n",
      "(nbs, nue): (8, 72)\n",
      "(nbs, nue): (8, 73)\n",
      "(nbs, nue): (8, 74)\n",
      "(nbs, nue): (8, 75)\n",
      "(nbs, nue): (9, 72)\n",
      "(nbs, nue): (9, 73)\n",
      "(nbs, nue): (9, 74)\n",
      "(nbs, nue): (9, 75)\n",
      "(nbs, nue): (10, 72)\n",
      "(nbs, nue): (10, 73)\n",
      "(nbs, nue): (10, 74)\n",
      "(nbs, nue): (10, 75)\n",
      "(nbs, nue): (11, 72)\n",
      "(nbs, nue): (11, 73)\n",
      "(nbs, nue): (11, 74)\n",
      "(nbs, nue): (11, 75)\n",
      "(nbs, nue): (12, 72)\n",
      "(nbs, nue): (12, 73)\n",
      "(nbs, nue): (12, 74)\n",
      "(nbs, nue): (12, 75)\n",
      "(nbs, nue): (13, 72)\n",
      "(nbs, nue): (13, 73)\n",
      "(nbs, nue): (13, 74)\n",
      "(nbs, nue): (13, 75)\n",
      "(nbs, nue): (14, 72)\n",
      "(nbs, nue): (14, 73)\n",
      "(nbs, nue): (14, 74)\n",
      "(nbs, nue): (14, 75)\n",
      "(nbs, nue): (15, 72)\n",
      "(nbs, nue): (15, 73)\n",
      "(nbs, nue): (15, 74)\n",
      "(nbs, nue): (15, 75)\n",
      "(nbs, nue): (16, 72)\n",
      "(nbs, nue): (16, 73)\n",
      "(nbs, nue): (16, 74)\n",
      "(nbs, nue): (16, 75)\n",
      "(nbs, nue): (17, 72)\n",
      "(nbs, nue): (17, 73)\n",
      "(nbs, nue): (17, 74)\n",
      "(nbs, nue): (17, 75)\n",
      "(nbs, nue): (18, 72)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(nbs, nue): (18, 73)\n",
      "(nbs, nue): (18, 74)\n",
      "(nbs, nue): (18, 75)\n",
      "(nbs, nue): (19, 72)\n",
      "(nbs, nue): (19, 73)\n",
      "(nbs, nue): (19, 74)\n",
      "(nbs, nue): (19, 75)\n",
      "(nbs, nue): (20, 72)\n",
      "(nbs, nue): (20, 73)\n",
      "(nbs, nue): (20, 74)\n",
      "(nbs, nue): (20, 75)\n",
      "***********  [18]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [19]-Passed Through Channel *********** \n",
      "***********  [19]-Noise Added *********** \n",
      "***********  [19]-Resource Grid Extracted *********** \n",
      "UE-Index: 76 | slot-Index: 19\n",
      "UE-Index: 77 | slot-Index: 19\n",
      "UE-Index: 78 | slot-Index: 19\n",
      "UE-Index: 79 | slot-Index: 19\n",
      "***********  [19]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 76)\n",
      "(nbs, nue): (0, 77)\n",
      "(nbs, nue): (0, 78)\n",
      "(nbs, nue): (0, 79)\n",
      "(nbs, nue): (1, 76)\n",
      "(nbs, nue): (1, 77)\n",
      "(nbs, nue): (1, 78)\n",
      "(nbs, nue): (1, 79)\n",
      "(nbs, nue): (2, 76)\n",
      "(nbs, nue): (2, 77)\n",
      "(nbs, nue): (2, 78)\n",
      "(nbs, nue): (2, 79)\n",
      "(nbs, nue): (3, 76)\n",
      "(nbs, nue): (3, 77)\n",
      "(nbs, nue): (3, 78)\n",
      "(nbs, nue): (3, 79)\n",
      "(nbs, nue): (4, 76)\n",
      "(nbs, nue): (4, 77)\n",
      "(nbs, nue): (4, 78)\n",
      "(nbs, nue): (4, 79)\n",
      "(nbs, nue): (5, 76)\n",
      "(nbs, nue): (5, 77)\n",
      "(nbs, nue): (5, 78)\n",
      "(nbs, nue): (5, 79)\n",
      "(nbs, nue): (6, 76)\n",
      "(nbs, nue): (6, 77)\n",
      "(nbs, nue): (6, 78)\n",
      "(nbs, nue): (6, 79)\n",
      "(nbs, nue): (7, 76)\n",
      "(nbs, nue): (7, 77)\n",
      "(nbs, nue): (7, 78)\n",
      "(nbs, nue): (7, 79)\n",
      "(nbs, nue): (8, 76)\n",
      "(nbs, nue): (8, 77)\n",
      "(nbs, nue): (8, 78)\n",
      "(nbs, nue): (8, 79)\n",
      "(nbs, nue): (9, 76)\n",
      "(nbs, nue): (9, 77)\n",
      "(nbs, nue): (9, 78)\n",
      "(nbs, nue): (9, 79)\n",
      "(nbs, nue): (10, 76)\n",
      "(nbs, nue): (10, 77)\n",
      "(nbs, nue): (10, 78)\n",
      "(nbs, nue): (10, 79)\n",
      "(nbs, nue): (11, 76)\n",
      "(nbs, nue): (11, 77)\n",
      "(nbs, nue): (11, 78)\n",
      "(nbs, nue): (11, 79)\n",
      "(nbs, nue): (12, 76)\n",
      "(nbs, nue): (12, 77)\n",
      "(nbs, nue): (12, 78)\n",
      "(nbs, nue): (12, 79)\n",
      "(nbs, nue): (13, 76)\n",
      "(nbs, nue): (13, 77)\n",
      "(nbs, nue): (13, 78)\n",
      "(nbs, nue): (13, 79)\n",
      "(nbs, nue): (14, 76)\n",
      "(nbs, nue): (14, 77)\n",
      "(nbs, nue): (14, 78)\n",
      "(nbs, nue): (14, 79)\n",
      "(nbs, nue): (15, 76)\n",
      "(nbs, nue): (15, 77)\n",
      "(nbs, nue): (15, 78)\n",
      "(nbs, nue): (15, 79)\n",
      "(nbs, nue): (16, 76)\n",
      "(nbs, nue): (16, 77)\n",
      "(nbs, nue): (16, 78)\n",
      "(nbs, nue): (16, 79)\n",
      "(nbs, nue): (17, 76)\n",
      "(nbs, nue): (17, 77)\n",
      "(nbs, nue): (17, 78)\n",
      "(nbs, nue): (17, 79)\n",
      "(nbs, nue): (18, 76)\n",
      "(nbs, nue): (18, 77)\n",
      "(nbs, nue): (18, 78)\n",
      "(nbs, nue): (18, 79)\n",
      "(nbs, nue): (19, 76)\n",
      "(nbs, nue): (19, 77)\n",
      "(nbs, nue): (19, 78)\n",
      "(nbs, nue): (19, 79)\n",
      "(nbs, nue): (20, 76)\n",
      "(nbs, nue): (20, 77)\n",
      "(nbs, nue): (20, 78)\n",
      "(nbs, nue): (20, 79)\n",
      "***********  [19]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [20]-Passed Through Channel *********** \n",
      "***********  [20]-Noise Added *********** \n",
      "***********  [20]-Resource Grid Extracted *********** \n",
      "UE-Index: 80 | slot-Index: 20\n",
      "UE-Index: 81 | slot-Index: 20\n",
      "UE-Index: 82 | slot-Index: 20\n",
      "UE-Index: 83 | slot-Index: 20\n",
      "***********  [20]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 80)\n",
      "(nbs, nue): (0, 81)\n",
      "(nbs, nue): (0, 82)\n",
      "(nbs, nue): (0, 83)\n",
      "(nbs, nue): (1, 80)\n",
      "(nbs, nue): (1, 81)\n",
      "(nbs, nue): (1, 82)\n",
      "(nbs, nue): (1, 83)\n",
      "(nbs, nue): (2, 80)\n",
      "(nbs, nue): (2, 81)\n",
      "(nbs, nue): (2, 82)\n",
      "(nbs, nue): (2, 83)\n",
      "(nbs, nue): (3, 80)\n",
      "(nbs, nue): (3, 81)\n",
      "(nbs, nue): (3, 82)\n",
      "(nbs, nue): (3, 83)\n",
      "(nbs, nue): (4, 80)\n",
      "(nbs, nue): (4, 81)\n",
      "(nbs, nue): (4, 82)\n",
      "(nbs, nue): (4, 83)\n",
      "(nbs, nue): (5, 80)\n",
      "(nbs, nue): (5, 81)\n",
      "(nbs, nue): (5, 82)\n",
      "(nbs, nue): (5, 83)\n",
      "(nbs, nue): (6, 80)\n",
      "(nbs, nue): (6, 81)\n",
      "(nbs, nue): (6, 82)\n",
      "(nbs, nue): (6, 83)\n",
      "(nbs, nue): (7, 80)\n",
      "(nbs, nue): (7, 81)\n",
      "(nbs, nue): (7, 82)\n",
      "(nbs, nue): (7, 83)\n",
      "(nbs, nue): (8, 80)\n",
      "(nbs, nue): (8, 81)\n",
      "(nbs, nue): (8, 82)\n",
      "(nbs, nue): (8, 83)\n",
      "(nbs, nue): (9, 80)\n",
      "(nbs, nue): (9, 81)\n",
      "(nbs, nue): (9, 82)\n",
      "(nbs, nue): (9, 83)\n",
      "(nbs, nue): (10, 80)\n",
      "(nbs, nue): (10, 81)\n",
      "(nbs, nue): (10, 82)\n",
      "(nbs, nue): (10, 83)\n",
      "(nbs, nue): (11, 80)\n",
      "(nbs, nue): (11, 81)\n",
      "(nbs, nue): (11, 82)\n",
      "(nbs, nue): (11, 83)\n",
      "(nbs, nue): (12, 80)\n",
      "(nbs, nue): (12, 81)\n",
      "(nbs, nue): (12, 82)\n",
      "(nbs, nue): (12, 83)\n",
      "(nbs, nue): (13, 80)\n",
      "(nbs, nue): (13, 81)\n",
      "(nbs, nue): (13, 82)\n",
      "(nbs, nue): (13, 83)\n",
      "(nbs, nue): (14, 80)\n",
      "(nbs, nue): (14, 81)\n",
      "(nbs, nue): (14, 82)\n",
      "(nbs, nue): (14, 83)\n",
      "(nbs, nue): (15, 80)\n",
      "(nbs, nue): (15, 81)\n",
      "(nbs, nue): (15, 82)\n",
      "(nbs, nue): (15, 83)\n",
      "(nbs, nue): (16, 80)\n",
      "(nbs, nue): (16, 81)\n",
      "(nbs, nue): (16, 82)\n",
      "(nbs, nue): (16, 83)\n",
      "(nbs, nue): (17, 80)\n",
      "(nbs, nue): (17, 81)\n",
      "(nbs, nue): (17, 82)\n",
      "(nbs, nue): (17, 83)\n",
      "(nbs, nue): (18, 80)\n",
      "(nbs, nue): (18, 81)\n",
      "(nbs, nue): (18, 82)\n",
      "(nbs, nue): (18, 83)\n",
      "(nbs, nue): (19, 80)\n",
      "(nbs, nue): (19, 81)\n",
      "(nbs, nue): (19, 82)\n",
      "(nbs, nue): (19, 83)\n",
      "(nbs, nue): (20, 80)\n",
      "(nbs, nue): (20, 81)\n",
      "(nbs, nue): (20, 82)\n",
      "(nbs, nue): (20, 83)\n",
      "***********  [20]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [21]-Passed Through Channel *********** \n",
      "***********  [21]-Noise Added *********** \n",
      "***********  [21]-Resource Grid Extracted *********** \n",
      "UE-Index: 84 | slot-Index: 21\n",
      "UE-Index: 85 | slot-Index: 21\n",
      "UE-Index: 86 | slot-Index: 21\n",
      "UE-Index: 87 | slot-Index: 21\n",
      "***********  [21]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 84)\n",
      "(nbs, nue): (0, 85)\n",
      "(nbs, nue): (0, 86)\n",
      "(nbs, nue): (0, 87)\n",
      "(nbs, nue): (1, 84)\n",
      "(nbs, nue): (1, 85)\n",
      "(nbs, nue): (1, 86)\n",
      "(nbs, nue): (1, 87)\n",
      "(nbs, nue): (2, 84)\n",
      "(nbs, nue): (2, 85)\n",
      "(nbs, nue): (2, 86)\n",
      "(nbs, nue): (2, 87)\n",
      "(nbs, nue): (3, 84)\n",
      "(nbs, nue): (3, 85)\n",
      "(nbs, nue): (3, 86)\n",
      "(nbs, nue): (3, 87)\n",
      "(nbs, nue): (4, 84)\n",
      "(nbs, nue): (4, 85)\n",
      "(nbs, nue): (4, 86)\n",
      "(nbs, nue): (4, 87)\n",
      "(nbs, nue): (5, 84)\n",
      "(nbs, nue): (5, 85)\n",
      "(nbs, nue): (5, 86)\n",
      "(nbs, nue): (5, 87)\n",
      "(nbs, nue): (6, 84)\n",
      "(nbs, nue): (6, 85)\n",
      "(nbs, nue): (6, 86)\n",
      "(nbs, nue): (6, 87)\n",
      "(nbs, nue): (7, 84)\n",
      "(nbs, nue): (7, 85)\n",
      "(nbs, nue): (7, 86)\n",
      "(nbs, nue): (7, 87)\n",
      "(nbs, nue): (8, 84)\n",
      "(nbs, nue): (8, 85)\n",
      "(nbs, nue): (8, 86)\n",
      "(nbs, nue): (8, 87)\n",
      "(nbs, nue): (9, 84)\n",
      "(nbs, nue): (9, 85)\n",
      "(nbs, nue): (9, 86)\n",
      "(nbs, nue): (9, 87)\n",
      "(nbs, nue): (10, 84)\n",
      "(nbs, nue): (10, 85)\n",
      "(nbs, nue): (10, 86)\n",
      "(nbs, nue): (10, 87)\n",
      "(nbs, nue): (11, 84)\n",
      "(nbs, nue): (11, 85)\n",
      "(nbs, nue): (11, 86)\n",
      "(nbs, nue): (11, 87)\n",
      "(nbs, nue): (12, 84)\n",
      "(nbs, nue): (12, 85)\n",
      "(nbs, nue): (12, 86)\n",
      "(nbs, nue): (12, 87)\n",
      "(nbs, nue): (13, 84)\n",
      "(nbs, nue): (13, 85)\n",
      "(nbs, nue): (13, 86)\n",
      "(nbs, nue): (13, 87)\n",
      "(nbs, nue): (14, 84)\n",
      "(nbs, nue): (14, 85)\n",
      "(nbs, nue): (14, 86)\n",
      "(nbs, nue): (14, 87)\n",
      "(nbs, nue): (15, 84)\n",
      "(nbs, nue): (15, 85)\n",
      "(nbs, nue): (15, 86)\n",
      "(nbs, nue): (15, 87)\n",
      "(nbs, nue): (16, 84)\n",
      "(nbs, nue): (16, 85)\n",
      "(nbs, nue): (16, 86)\n",
      "(nbs, nue): (16, 87)\n",
      "(nbs, nue): (17, 84)\n",
      "(nbs, nue): (17, 85)\n",
      "(nbs, nue): (17, 86)\n",
      "(nbs, nue): (17, 87)\n",
      "(nbs, nue): (18, 84)\n",
      "(nbs, nue): (18, 85)\n",
      "(nbs, nue): (18, 86)\n",
      "(nbs, nue): (18, 87)\n",
      "(nbs, nue): (19, 84)\n",
      "(nbs, nue): (19, 85)\n",
      "(nbs, nue): (19, 86)\n",
      "(nbs, nue): (19, 87)\n",
      "(nbs, nue): (20, 84)\n",
      "(nbs, nue): (20, 85)\n",
      "(nbs, nue): (20, 86)\n",
      "(nbs, nue): (20, 87)\n",
      "***********  [21]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [22]-Passed Through Channel *********** \n",
      "***********  [22]-Noise Added *********** \n",
      "***********  [22]-Resource Grid Extracted *********** \n",
      "UE-Index: 88 | slot-Index: 22\n",
      "UE-Index: 89 | slot-Index: 22\n",
      "UE-Index: 90 | slot-Index: 22\n",
      "UE-Index: 91 | slot-Index: 22\n",
      "***********  [22]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 88)\n",
      "(nbs, nue): (0, 89)\n",
      "(nbs, nue): (0, 90)\n",
      "(nbs, nue): (0, 91)\n",
      "(nbs, nue): (1, 88)\n",
      "(nbs, nue): (1, 89)\n",
      "(nbs, nue): (1, 90)\n",
      "(nbs, nue): (1, 91)\n",
      "(nbs, nue): (2, 88)\n",
      "(nbs, nue): (2, 89)\n",
      "(nbs, nue): (2, 90)\n",
      "(nbs, nue): (2, 91)\n",
      "(nbs, nue): (3, 88)\n",
      "(nbs, nue): (3, 89)\n",
      "(nbs, nue): (3, 90)\n",
      "(nbs, nue): (3, 91)\n",
      "(nbs, nue): (4, 88)\n",
      "(nbs, nue): (4, 89)\n",
      "(nbs, nue): (4, 90)\n",
      "(nbs, nue): (4, 91)\n",
      "(nbs, nue): (5, 88)\n",
      "(nbs, nue): (5, 89)\n",
      "(nbs, nue): (5, 90)\n",
      "(nbs, nue): (5, 91)\n",
      "(nbs, nue): (6, 88)\n",
      "(nbs, nue): (6, 89)\n",
      "(nbs, nue): (6, 90)\n",
      "(nbs, nue): (6, 91)\n",
      "(nbs, nue): (7, 88)\n",
      "(nbs, nue): (7, 89)\n",
      "(nbs, nue): (7, 90)\n",
      "(nbs, nue): (7, 91)\n",
      "(nbs, nue): (8, 88)\n",
      "(nbs, nue): (8, 89)\n",
      "(nbs, nue): (8, 90)\n",
      "(nbs, nue): (8, 91)\n",
      "(nbs, nue): (9, 88)\n",
      "(nbs, nue): (9, 89)\n",
      "(nbs, nue): (9, 90)\n",
      "(nbs, nue): (9, 91)\n",
      "(nbs, nue): (10, 88)\n",
      "(nbs, nue): (10, 89)\n",
      "(nbs, nue): (10, 90)\n",
      "(nbs, nue): (10, 91)\n",
      "(nbs, nue): (11, 88)\n",
      "(nbs, nue): (11, 89)\n",
      "(nbs, nue): (11, 90)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(nbs, nue): (11, 91)\n",
      "(nbs, nue): (12, 88)\n",
      "(nbs, nue): (12, 89)\n",
      "(nbs, nue): (12, 90)\n",
      "(nbs, nue): (12, 91)\n",
      "(nbs, nue): (13, 88)\n",
      "(nbs, nue): (13, 89)\n",
      "(nbs, nue): (13, 90)\n",
      "(nbs, nue): (13, 91)\n",
      "(nbs, nue): (14, 88)\n",
      "(nbs, nue): (14, 89)\n",
      "(nbs, nue): (14, 90)\n",
      "(nbs, nue): (14, 91)\n",
      "(nbs, nue): (15, 88)\n",
      "(nbs, nue): (15, 89)\n",
      "(nbs, nue): (15, 90)\n",
      "(nbs, nue): (15, 91)\n",
      "(nbs, nue): (16, 88)\n",
      "(nbs, nue): (16, 89)\n",
      "(nbs, nue): (16, 90)\n",
      "(nbs, nue): (16, 91)\n",
      "(nbs, nue): (17, 88)\n",
      "(nbs, nue): (17, 89)\n",
      "(nbs, nue): (17, 90)\n",
      "(nbs, nue): (17, 91)\n",
      "(nbs, nue): (18, 88)\n",
      "(nbs, nue): (18, 89)\n",
      "(nbs, nue): (18, 90)\n",
      "(nbs, nue): (18, 91)\n",
      "(nbs, nue): (19, 88)\n",
      "(nbs, nue): (19, 89)\n",
      "(nbs, nue): (19, 90)\n",
      "(nbs, nue): (19, 91)\n",
      "(nbs, nue): (20, 88)\n",
      "(nbs, nue): (20, 89)\n",
      "(nbs, nue): (20, 90)\n",
      "(nbs, nue): (20, 91)\n",
      "***********  [22]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [23]-Passed Through Channel *********** \n",
      "***********  [23]-Noise Added *********** \n",
      "***********  [23]-Resource Grid Extracted *********** \n",
      "UE-Index: 92 | slot-Index: 23\n",
      "UE-Index: 93 | slot-Index: 23\n",
      "UE-Index: 94 | slot-Index: 23\n",
      "UE-Index: 95 | slot-Index: 23\n",
      "***********  [23]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 92)\n",
      "(nbs, nue): (0, 93)\n",
      "(nbs, nue): (0, 94)\n",
      "(nbs, nue): (0, 95)\n",
      "(nbs, nue): (1, 92)\n",
      "(nbs, nue): (1, 93)\n",
      "(nbs, nue): (1, 94)\n",
      "(nbs, nue): (1, 95)\n",
      "(nbs, nue): (2, 92)\n",
      "(nbs, nue): (2, 93)\n",
      "(nbs, nue): (2, 94)\n",
      "(nbs, nue): (2, 95)\n",
      "(nbs, nue): (3, 92)\n",
      "(nbs, nue): (3, 93)\n",
      "(nbs, nue): (3, 94)\n",
      "(nbs, nue): (3, 95)\n",
      "(nbs, nue): (4, 92)\n",
      "(nbs, nue): (4, 93)\n",
      "(nbs, nue): (4, 94)\n",
      "(nbs, nue): (4, 95)\n",
      "(nbs, nue): (5, 92)\n",
      "(nbs, nue): (5, 93)\n",
      "(nbs, nue): (5, 94)\n",
      "(nbs, nue): (5, 95)\n",
      "(nbs, nue): (6, 92)\n",
      "(nbs, nue): (6, 93)\n",
      "(nbs, nue): (6, 94)\n",
      "(nbs, nue): (6, 95)\n",
      "(nbs, nue): (7, 92)\n",
      "(nbs, nue): (7, 93)\n",
      "(nbs, nue): (7, 94)\n",
      "(nbs, nue): (7, 95)\n",
      "(nbs, nue): (8, 92)\n",
      "(nbs, nue): (8, 93)\n",
      "(nbs, nue): (8, 94)\n",
      "(nbs, nue): (8, 95)\n",
      "(nbs, nue): (9, 92)\n",
      "(nbs, nue): (9, 93)\n",
      "(nbs, nue): (9, 94)\n",
      "(nbs, nue): (9, 95)\n",
      "(nbs, nue): (10, 92)\n",
      "(nbs, nue): (10, 93)\n",
      "(nbs, nue): (10, 94)\n",
      "(nbs, nue): (10, 95)\n",
      "(nbs, nue): (11, 92)\n",
      "(nbs, nue): (11, 93)\n",
      "(nbs, nue): (11, 94)\n",
      "(nbs, nue): (11, 95)\n",
      "(nbs, nue): (12, 92)\n",
      "(nbs, nue): (12, 93)\n",
      "(nbs, nue): (12, 94)\n",
      "(nbs, nue): (12, 95)\n",
      "(nbs, nue): (13, 92)\n",
      "(nbs, nue): (13, 93)\n",
      "(nbs, nue): (13, 94)\n",
      "(nbs, nue): (13, 95)\n",
      "(nbs, nue): (14, 92)\n",
      "(nbs, nue): (14, 93)\n",
      "(nbs, nue): (14, 94)\n",
      "(nbs, nue): (14, 95)\n",
      "(nbs, nue): (15, 92)\n",
      "(nbs, nue): (15, 93)\n",
      "(nbs, nue): (15, 94)\n",
      "(nbs, nue): (15, 95)\n",
      "(nbs, nue): (16, 92)\n",
      "(nbs, nue): (16, 93)\n",
      "(nbs, nue): (16, 94)\n",
      "(nbs, nue): (16, 95)\n",
      "(nbs, nue): (17, 92)\n",
      "(nbs, nue): (17, 93)\n",
      "(nbs, nue): (17, 94)\n",
      "(nbs, nue): (17, 95)\n",
      "(nbs, nue): (18, 92)\n",
      "(nbs, nue): (18, 93)\n",
      "(nbs, nue): (18, 94)\n",
      "(nbs, nue): (18, 95)\n",
      "(nbs, nue): (19, 92)\n",
      "(nbs, nue): (19, 93)\n",
      "(nbs, nue): (19, 94)\n",
      "(nbs, nue): (19, 95)\n",
      "(nbs, nue): (20, 92)\n",
      "(nbs, nue): (20, 93)\n",
      "(nbs, nue): (20, 94)\n",
      "(nbs, nue): (20, 95)\n",
      "***********  [23]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [24]-Passed Through Channel *********** \n",
      "***********  [24]-Noise Added *********** \n",
      "***********  [24]-Resource Grid Extracted *********** \n",
      "UE-Index: 96 | slot-Index: 24\n",
      "UE-Index: 97 | slot-Index: 24\n",
      "UE-Index: 98 | slot-Index: 24\n",
      "UE-Index: 99 | slot-Index: 24\n",
      "***********  [24]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 96)\n",
      "(nbs, nue): (0, 97)\n",
      "(nbs, nue): (0, 98)\n",
      "(nbs, nue): (0, 99)\n",
      "(nbs, nue): (1, 96)\n",
      "(nbs, nue): (1, 97)\n",
      "(nbs, nue): (1, 98)\n",
      "(nbs, nue): (1, 99)\n",
      "(nbs, nue): (2, 96)\n",
      "(nbs, nue): (2, 97)\n",
      "(nbs, nue): (2, 98)\n",
      "(nbs, nue): (2, 99)\n",
      "(nbs, nue): (3, 96)\n",
      "(nbs, nue): (3, 97)\n",
      "(nbs, nue): (3, 98)\n",
      "(nbs, nue): (3, 99)\n",
      "(nbs, nue): (4, 96)\n",
      "(nbs, nue): (4, 97)\n",
      "(nbs, nue): (4, 98)\n",
      "(nbs, nue): (4, 99)\n",
      "(nbs, nue): (5, 96)\n",
      "(nbs, nue): (5, 97)\n",
      "(nbs, nue): (5, 98)\n",
      "(nbs, nue): (5, 99)\n",
      "(nbs, nue): (6, 96)\n",
      "(nbs, nue): (6, 97)\n",
      "(nbs, nue): (6, 98)\n",
      "(nbs, nue): (6, 99)\n",
      "(nbs, nue): (7, 96)\n",
      "(nbs, nue): (7, 97)\n",
      "(nbs, nue): (7, 98)\n",
      "(nbs, nue): (7, 99)\n",
      "(nbs, nue): (8, 96)\n",
      "(nbs, nue): (8, 97)\n",
      "(nbs, nue): (8, 98)\n",
      "(nbs, nue): (8, 99)\n",
      "(nbs, nue): (9, 96)\n",
      "(nbs, nue): (9, 97)\n",
      "(nbs, nue): (9, 98)\n",
      "(nbs, nue): (9, 99)\n",
      "(nbs, nue): (10, 96)\n",
      "(nbs, nue): (10, 97)\n",
      "(nbs, nue): (10, 98)\n",
      "(nbs, nue): (10, 99)\n",
      "(nbs, nue): (11, 96)\n",
      "(nbs, nue): (11, 97)\n",
      "(nbs, nue): (11, 98)\n",
      "(nbs, nue): (11, 99)\n",
      "(nbs, nue): (12, 96)\n",
      "(nbs, nue): (12, 97)\n",
      "(nbs, nue): (12, 98)\n",
      "(nbs, nue): (12, 99)\n",
      "(nbs, nue): (13, 96)\n",
      "(nbs, nue): (13, 97)\n",
      "(nbs, nue): (13, 98)\n",
      "(nbs, nue): (13, 99)\n",
      "(nbs, nue): (14, 96)\n",
      "(nbs, nue): (14, 97)\n",
      "(nbs, nue): (14, 98)\n",
      "(nbs, nue): (14, 99)\n",
      "(nbs, nue): (15, 96)\n",
      "(nbs, nue): (15, 97)\n",
      "(nbs, nue): (15, 98)\n",
      "(nbs, nue): (15, 99)\n",
      "(nbs, nue): (16, 96)\n",
      "(nbs, nue): (16, 97)\n",
      "(nbs, nue): (16, 98)\n",
      "(nbs, nue): (16, 99)\n",
      "(nbs, nue): (17, 96)\n",
      "(nbs, nue): (17, 97)\n",
      "(nbs, nue): (17, 98)\n",
      "(nbs, nue): (17, 99)\n",
      "(nbs, nue): (18, 96)\n",
      "(nbs, nue): (18, 97)\n",
      "(nbs, nue): (18, 98)\n",
      "(nbs, nue): (18, 99)\n",
      "(nbs, nue): (19, 96)\n",
      "(nbs, nue): (19, 97)\n",
      "(nbs, nue): (19, 98)\n",
      "(nbs, nue): (19, 99)\n",
      "(nbs, nue): (20, 96)\n",
      "(nbs, nue): (20, 97)\n",
      "(nbs, nue): (20, 98)\n",
      "(nbs, nue): (20, 99)\n",
      "***********  [24]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [25]-Passed Through Channel *********** \n",
      "***********  [25]-Noise Added *********** \n",
      "***********  [25]-Resource Grid Extracted *********** \n",
      "UE-Index: 100 | slot-Index: 25\n",
      "UE-Index: 101 | slot-Index: 25\n",
      "UE-Index: 102 | slot-Index: 25\n",
      "UE-Index: 103 | slot-Index: 25\n",
      "***********  [25]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 100)\n",
      "(nbs, nue): (0, 101)\n",
      "(nbs, nue): (0, 102)\n",
      "(nbs, nue): (0, 103)\n",
      "(nbs, nue): (1, 100)\n",
      "(nbs, nue): (1, 101)\n",
      "(nbs, nue): (1, 102)\n",
      "(nbs, nue): (1, 103)\n",
      "(nbs, nue): (2, 100)\n",
      "(nbs, nue): (2, 101)\n",
      "(nbs, nue): (2, 102)\n",
      "(nbs, nue): (2, 103)\n",
      "(nbs, nue): (3, 100)\n",
      "(nbs, nue): (3, 101)\n",
      "(nbs, nue): (3, 102)\n",
      "(nbs, nue): (3, 103)\n",
      "(nbs, nue): (4, 100)\n",
      "(nbs, nue): (4, 101)\n",
      "(nbs, nue): (4, 102)\n",
      "(nbs, nue): (4, 103)\n",
      "(nbs, nue): (5, 100)\n",
      "(nbs, nue): (5, 101)\n",
      "(nbs, nue): (5, 102)\n",
      "(nbs, nue): (5, 103)\n",
      "(nbs, nue): (6, 100)\n",
      "(nbs, nue): (6, 101)\n",
      "(nbs, nue): (6, 102)\n",
      "(nbs, nue): (6, 103)\n",
      "(nbs, nue): (7, 100)\n",
      "(nbs, nue): (7, 101)\n",
      "(nbs, nue): (7, 102)\n",
      "(nbs, nue): (7, 103)\n",
      "(nbs, nue): (8, 100)\n",
      "(nbs, nue): (8, 101)\n",
      "(nbs, nue): (8, 102)\n",
      "(nbs, nue): (8, 103)\n",
      "(nbs, nue): (9, 100)\n",
      "(nbs, nue): (9, 101)\n",
      "(nbs, nue): (9, 102)\n",
      "(nbs, nue): (9, 103)\n",
      "(nbs, nue): (10, 100)\n",
      "(nbs, nue): (10, 101)\n",
      "(nbs, nue): (10, 102)\n",
      "(nbs, nue): (10, 103)\n",
      "(nbs, nue): (11, 100)\n",
      "(nbs, nue): (11, 101)\n",
      "(nbs, nue): (11, 102)\n",
      "(nbs, nue): (11, 103)\n",
      "(nbs, nue): (12, 100)\n",
      "(nbs, nue): (12, 101)\n",
      "(nbs, nue): (12, 102)\n",
      "(nbs, nue): (12, 103)\n",
      "(nbs, nue): (13, 100)\n",
      "(nbs, nue): (13, 101)\n",
      "(nbs, nue): (13, 102)\n",
      "(nbs, nue): (13, 103)\n",
      "(nbs, nue): (14, 100)\n",
      "(nbs, nue): (14, 101)\n",
      "(nbs, nue): (14, 102)\n",
      "(nbs, nue): (14, 103)\n",
      "(nbs, nue): (15, 100)\n",
      "(nbs, nue): (15, 101)\n",
      "(nbs, nue): (15, 102)\n",
      "(nbs, nue): (15, 103)\n",
      "(nbs, nue): (16, 100)\n",
      "(nbs, nue): (16, 101)\n",
      "(nbs, nue): (16, 102)\n",
      "(nbs, nue): (16, 103)\n",
      "(nbs, nue): (17, 100)\n",
      "(nbs, nue): (17, 101)\n",
      "(nbs, nue): (17, 102)\n",
      "(nbs, nue): (17, 103)\n",
      "(nbs, nue): (18, 100)\n",
      "(nbs, nue): (18, 101)\n",
      "(nbs, nue): (18, 102)\n",
      "(nbs, nue): (18, 103)\n",
      "(nbs, nue): (19, 100)\n",
      "(nbs, nue): (19, 101)\n",
      "(nbs, nue): (19, 102)\n",
      "(nbs, nue): (19, 103)\n",
      "(nbs, nue): (20, 100)\n",
      "(nbs, nue): (20, 101)\n",
      "(nbs, nue): (20, 102)\n",
      "(nbs, nue): (20, 103)\n",
      "***********  [25]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [26]-Passed Through Channel *********** \n",
      "***********  [26]-Noise Added *********** \n",
      "***********  [26]-Resource Grid Extracted *********** \n",
      "UE-Index: 104 | slot-Index: 26\n",
      "UE-Index: 105 | slot-Index: 26\n",
      "UE-Index: 106 | slot-Index: 26\n",
      "UE-Index: 107 | slot-Index: 26\n",
      "***********  [26]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 104)\n",
      "(nbs, nue): (0, 105)\n",
      "(nbs, nue): (0, 106)\n",
      "(nbs, nue): (0, 107)\n",
      "(nbs, nue): (1, 104)\n",
      "(nbs, nue): (1, 105)\n",
      "(nbs, nue): (1, 106)\n",
      "(nbs, nue): (1, 107)\n",
      "(nbs, nue): (2, 104)\n",
      "(nbs, nue): (2, 105)\n",
      "(nbs, nue): (2, 106)\n",
      "(nbs, nue): (2, 107)\n",
      "(nbs, nue): (3, 104)\n",
      "(nbs, nue): (3, 105)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(nbs, nue): (3, 106)\n",
      "(nbs, nue): (3, 107)\n",
      "(nbs, nue): (4, 104)\n",
      "(nbs, nue): (4, 105)\n",
      "(nbs, nue): (4, 106)\n",
      "(nbs, nue): (4, 107)\n",
      "(nbs, nue): (5, 104)\n",
      "(nbs, nue): (5, 105)\n",
      "(nbs, nue): (5, 106)\n",
      "(nbs, nue): (5, 107)\n",
      "(nbs, nue): (6, 104)\n",
      "(nbs, nue): (6, 105)\n",
      "(nbs, nue): (6, 106)\n",
      "(nbs, nue): (6, 107)\n",
      "(nbs, nue): (7, 104)\n",
      "(nbs, nue): (7, 105)\n",
      "(nbs, nue): (7, 106)\n",
      "(nbs, nue): (7, 107)\n",
      "(nbs, nue): (8, 104)\n",
      "(nbs, nue): (8, 105)\n",
      "(nbs, nue): (8, 106)\n",
      "(nbs, nue): (8, 107)\n",
      "(nbs, nue): (9, 104)\n",
      "(nbs, nue): (9, 105)\n",
      "(nbs, nue): (9, 106)\n",
      "(nbs, nue): (9, 107)\n",
      "(nbs, nue): (10, 104)\n",
      "(nbs, nue): (10, 105)\n",
      "(nbs, nue): (10, 106)\n",
      "(nbs, nue): (10, 107)\n",
      "(nbs, nue): (11, 104)\n",
      "(nbs, nue): (11, 105)\n",
      "(nbs, nue): (11, 106)\n",
      "(nbs, nue): (11, 107)\n",
      "(nbs, nue): (12, 104)\n",
      "(nbs, nue): (12, 105)\n",
      "(nbs, nue): (12, 106)\n",
      "(nbs, nue): (12, 107)\n",
      "(nbs, nue): (13, 104)\n",
      "(nbs, nue): (13, 105)\n",
      "(nbs, nue): (13, 106)\n",
      "(nbs, nue): (13, 107)\n",
      "(nbs, nue): (14, 104)\n",
      "(nbs, nue): (14, 105)\n",
      "(nbs, nue): (14, 106)\n",
      "(nbs, nue): (14, 107)\n",
      "(nbs, nue): (15, 104)\n",
      "(nbs, nue): (15, 105)\n",
      "(nbs, nue): (15, 106)\n",
      "(nbs, nue): (15, 107)\n",
      "(nbs, nue): (16, 104)\n",
      "(nbs, nue): (16, 105)\n",
      "(nbs, nue): (16, 106)\n",
      "(nbs, nue): (16, 107)\n",
      "(nbs, nue): (17, 104)\n",
      "(nbs, nue): (17, 105)\n",
      "(nbs, nue): (17, 106)\n",
      "(nbs, nue): (17, 107)\n",
      "(nbs, nue): (18, 104)\n",
      "(nbs, nue): (18, 105)\n",
      "(nbs, nue): (18, 106)\n",
      "(nbs, nue): (18, 107)\n",
      "(nbs, nue): (19, 104)\n",
      "(nbs, nue): (19, 105)\n",
      "(nbs, nue): (19, 106)\n",
      "(nbs, nue): (19, 107)\n",
      "(nbs, nue): (20, 104)\n",
      "(nbs, nue): (20, 105)\n",
      "(nbs, nue): (20, 106)\n",
      "(nbs, nue): (20, 107)\n",
      "***********  [26]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [27]-Passed Through Channel *********** \n",
      "***********  [27]-Noise Added *********** \n",
      "***********  [27]-Resource Grid Extracted *********** \n",
      "UE-Index: 108 | slot-Index: 27\n",
      "UE-Index: 109 | slot-Index: 27\n",
      "UE-Index: 110 | slot-Index: 27\n",
      "UE-Index: 111 | slot-Index: 27\n",
      "***********  [27]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 108)\n",
      "(nbs, nue): (0, 109)\n",
      "(nbs, nue): (0, 110)\n",
      "(nbs, nue): (0, 111)\n",
      "(nbs, nue): (1, 108)\n",
      "(nbs, nue): (1, 109)\n",
      "(nbs, nue): (1, 110)\n",
      "(nbs, nue): (1, 111)\n",
      "(nbs, nue): (2, 108)\n",
      "(nbs, nue): (2, 109)\n",
      "(nbs, nue): (2, 110)\n",
      "(nbs, nue): (2, 111)\n",
      "(nbs, nue): (3, 108)\n",
      "(nbs, nue): (3, 109)\n",
      "(nbs, nue): (3, 110)\n",
      "(nbs, nue): (3, 111)\n",
      "(nbs, nue): (4, 108)\n",
      "(nbs, nue): (4, 109)\n",
      "(nbs, nue): (4, 110)\n",
      "(nbs, nue): (4, 111)\n",
      "(nbs, nue): (5, 108)\n",
      "(nbs, nue): (5, 109)\n",
      "(nbs, nue): (5, 110)\n",
      "(nbs, nue): (5, 111)\n",
      "(nbs, nue): (6, 108)\n",
      "(nbs, nue): (6, 109)\n",
      "(nbs, nue): (6, 110)\n",
      "(nbs, nue): (6, 111)\n",
      "(nbs, nue): (7, 108)\n",
      "(nbs, nue): (7, 109)\n",
      "(nbs, nue): (7, 110)\n",
      "(nbs, nue): (7, 111)\n",
      "(nbs, nue): (8, 108)\n",
      "(nbs, nue): (8, 109)\n",
      "(nbs, nue): (8, 110)\n",
      "(nbs, nue): (8, 111)\n",
      "(nbs, nue): (9, 108)\n",
      "(nbs, nue): (9, 109)\n",
      "(nbs, nue): (9, 110)\n",
      "(nbs, nue): (9, 111)\n",
      "(nbs, nue): (10, 108)\n",
      "(nbs, nue): (10, 109)\n",
      "(nbs, nue): (10, 110)\n",
      "(nbs, nue): (10, 111)\n",
      "(nbs, nue): (11, 108)\n",
      "(nbs, nue): (11, 109)\n",
      "(nbs, nue): (11, 110)\n",
      "(nbs, nue): (11, 111)\n",
      "(nbs, nue): (12, 108)\n",
      "(nbs, nue): (12, 109)\n",
      "(nbs, nue): (12, 110)\n",
      "(nbs, nue): (12, 111)\n",
      "(nbs, nue): (13, 108)\n",
      "(nbs, nue): (13, 109)\n",
      "(nbs, nue): (13, 110)\n",
      "(nbs, nue): (13, 111)\n",
      "(nbs, nue): (14, 108)\n",
      "(nbs, nue): (14, 109)\n",
      "(nbs, nue): (14, 110)\n",
      "(nbs, nue): (14, 111)\n",
      "(nbs, nue): (15, 108)\n",
      "(nbs, nue): (15, 109)\n",
      "(nbs, nue): (15, 110)\n",
      "(nbs, nue): (15, 111)\n",
      "(nbs, nue): (16, 108)\n",
      "(nbs, nue): (16, 109)\n",
      "(nbs, nue): (16, 110)\n",
      "(nbs, nue): (16, 111)\n",
      "(nbs, nue): (17, 108)\n",
      "(nbs, nue): (17, 109)\n",
      "(nbs, nue): (17, 110)\n",
      "(nbs, nue): (17, 111)\n",
      "(nbs, nue): (18, 108)\n",
      "(nbs, nue): (18, 109)\n",
      "(nbs, nue): (18, 110)\n",
      "(nbs, nue): (18, 111)\n",
      "(nbs, nue): (19, 108)\n",
      "(nbs, nue): (19, 109)\n",
      "(nbs, nue): (19, 110)\n",
      "(nbs, nue): (19, 111)\n",
      "(nbs, nue): (20, 108)\n",
      "(nbs, nue): (20, 109)\n",
      "(nbs, nue): (20, 110)\n",
      "(nbs, nue): (20, 111)\n",
      "***********  [27]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [28]-Passed Through Channel *********** \n",
      "***********  [28]-Noise Added *********** \n",
      "***********  [28]-Resource Grid Extracted *********** \n",
      "UE-Index: 112 | slot-Index: 28\n",
      "UE-Index: 113 | slot-Index: 28\n",
      "UE-Index: 114 | slot-Index: 28\n",
      "UE-Index: 115 | slot-Index: 28\n",
      "***********  [28]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 112)\n",
      "(nbs, nue): (0, 113)\n",
      "(nbs, nue): (0, 114)\n",
      "(nbs, nue): (0, 115)\n",
      "(nbs, nue): (1, 112)\n",
      "(nbs, nue): (1, 113)\n",
      "(nbs, nue): (1, 114)\n",
      "(nbs, nue): (1, 115)\n",
      "(nbs, nue): (2, 112)\n",
      "(nbs, nue): (2, 113)\n",
      "(nbs, nue): (2, 114)\n",
      "(nbs, nue): (2, 115)\n",
      "(nbs, nue): (3, 112)\n",
      "(nbs, nue): (3, 113)\n",
      "(nbs, nue): (3, 114)\n",
      "(nbs, nue): (3, 115)\n",
      "(nbs, nue): (4, 112)\n",
      "(nbs, nue): (4, 113)\n",
      "(nbs, nue): (4, 114)\n",
      "(nbs, nue): (4, 115)\n",
      "(nbs, nue): (5, 112)\n",
      "(nbs, nue): (5, 113)\n",
      "(nbs, nue): (5, 114)\n",
      "(nbs, nue): (5, 115)\n",
      "(nbs, nue): (6, 112)\n",
      "(nbs, nue): (6, 113)\n",
      "(nbs, nue): (6, 114)\n",
      "(nbs, nue): (6, 115)\n",
      "(nbs, nue): (7, 112)\n",
      "(nbs, nue): (7, 113)\n",
      "(nbs, nue): (7, 114)\n",
      "(nbs, nue): (7, 115)\n",
      "(nbs, nue): (8, 112)\n",
      "(nbs, nue): (8, 113)\n",
      "(nbs, nue): (8, 114)\n",
      "(nbs, nue): (8, 115)\n",
      "(nbs, nue): (9, 112)\n",
      "(nbs, nue): (9, 113)\n",
      "(nbs, nue): (9, 114)\n",
      "(nbs, nue): (9, 115)\n",
      "(nbs, nue): (10, 112)\n",
      "(nbs, nue): (10, 113)\n",
      "(nbs, nue): (10, 114)\n",
      "(nbs, nue): (10, 115)\n",
      "(nbs, nue): (11, 112)\n",
      "(nbs, nue): (11, 113)\n",
      "(nbs, nue): (11, 114)\n",
      "(nbs, nue): (11, 115)\n",
      "(nbs, nue): (12, 112)\n",
      "(nbs, nue): (12, 113)\n",
      "(nbs, nue): (12, 114)\n",
      "(nbs, nue): (12, 115)\n",
      "(nbs, nue): (13, 112)\n",
      "(nbs, nue): (13, 113)\n",
      "(nbs, nue): (13, 114)\n",
      "(nbs, nue): (13, 115)\n",
      "(nbs, nue): (14, 112)\n",
      "(nbs, nue): (14, 113)\n",
      "(nbs, nue): (14, 114)\n",
      "(nbs, nue): (14, 115)\n",
      "(nbs, nue): (15, 112)\n",
      "(nbs, nue): (15, 113)\n",
      "(nbs, nue): (15, 114)\n",
      "(nbs, nue): (15, 115)\n",
      "(nbs, nue): (16, 112)\n",
      "(nbs, nue): (16, 113)\n",
      "(nbs, nue): (16, 114)\n",
      "(nbs, nue): (16, 115)\n",
      "(nbs, nue): (17, 112)\n",
      "(nbs, nue): (17, 113)\n",
      "(nbs, nue): (17, 114)\n",
      "(nbs, nue): (17, 115)\n",
      "(nbs, nue): (18, 112)\n",
      "(nbs, nue): (18, 113)\n",
      "(nbs, nue): (18, 114)\n",
      "(nbs, nue): (18, 115)\n",
      "(nbs, nue): (19, 112)\n",
      "(nbs, nue): (19, 113)\n",
      "(nbs, nue): (19, 114)\n",
      "(nbs, nue): (19, 115)\n",
      "(nbs, nue): (20, 112)\n",
      "(nbs, nue): (20, 113)\n",
      "(nbs, nue): (20, 114)\n",
      "(nbs, nue): (20, 115)\n",
      "***********  [28]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [29]-Passed Through Channel *********** \n",
      "***********  [29]-Noise Added *********** \n",
      "***********  [29]-Resource Grid Extracted *********** \n",
      "UE-Index: 116 | slot-Index: 29\n",
      "UE-Index: 117 | slot-Index: 29\n",
      "UE-Index: 118 | slot-Index: 29\n",
      "UE-Index: 119 | slot-Index: 29\n",
      "***********  [29]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 116)\n",
      "(nbs, nue): (0, 117)\n",
      "(nbs, nue): (0, 118)\n",
      "(nbs, nue): (0, 119)\n",
      "(nbs, nue): (1, 116)\n",
      "(nbs, nue): (1, 117)\n",
      "(nbs, nue): (1, 118)\n",
      "(nbs, nue): (1, 119)\n",
      "(nbs, nue): (2, 116)\n",
      "(nbs, nue): (2, 117)\n",
      "(nbs, nue): (2, 118)\n",
      "(nbs, nue): (2, 119)\n",
      "(nbs, nue): (3, 116)\n",
      "(nbs, nue): (3, 117)\n",
      "(nbs, nue): (3, 118)\n",
      "(nbs, nue): (3, 119)\n",
      "(nbs, nue): (4, 116)\n",
      "(nbs, nue): (4, 117)\n",
      "(nbs, nue): (4, 118)\n",
      "(nbs, nue): (4, 119)\n",
      "(nbs, nue): (5, 116)\n",
      "(nbs, nue): (5, 117)\n",
      "(nbs, nue): (5, 118)\n",
      "(nbs, nue): (5, 119)\n",
      "(nbs, nue): (6, 116)\n",
      "(nbs, nue): (6, 117)\n",
      "(nbs, nue): (6, 118)\n",
      "(nbs, nue): (6, 119)\n",
      "(nbs, nue): (7, 116)\n",
      "(nbs, nue): (7, 117)\n",
      "(nbs, nue): (7, 118)\n",
      "(nbs, nue): (7, 119)\n",
      "(nbs, nue): (8, 116)\n",
      "(nbs, nue): (8, 117)\n",
      "(nbs, nue): (8, 118)\n",
      "(nbs, nue): (8, 119)\n",
      "(nbs, nue): (9, 116)\n",
      "(nbs, nue): (9, 117)\n",
      "(nbs, nue): (9, 118)\n",
      "(nbs, nue): (9, 119)\n",
      "(nbs, nue): (10, 116)\n",
      "(nbs, nue): (10, 117)\n",
      "(nbs, nue): (10, 118)\n",
      "(nbs, nue): (10, 119)\n",
      "(nbs, nue): (11, 116)\n",
      "(nbs, nue): (11, 117)\n",
      "(nbs, nue): (11, 118)\n",
      "(nbs, nue): (11, 119)\n",
      "(nbs, nue): (12, 116)\n",
      "(nbs, nue): (12, 117)\n",
      "(nbs, nue): (12, 118)\n",
      "(nbs, nue): (12, 119)\n",
      "(nbs, nue): (13, 116)\n",
      "(nbs, nue): (13, 117)\n",
      "(nbs, nue): (13, 118)\n",
      "(nbs, nue): (13, 119)\n",
      "(nbs, nue): (14, 116)\n",
      "(nbs, nue): (14, 117)\n",
      "(nbs, nue): (14, 118)\n",
      "(nbs, nue): (14, 119)\n",
      "(nbs, nue): (15, 116)\n",
      "(nbs, nue): (15, 117)\n",
      "(nbs, nue): (15, 118)\n",
      "(nbs, nue): (15, 119)\n",
      "(nbs, nue): (16, 116)\n",
      "(nbs, nue): (16, 117)\n",
      "(nbs, nue): (16, 118)\n",
      "(nbs, nue): (16, 119)\n",
      "(nbs, nue): (17, 116)\n",
      "(nbs, nue): (17, 117)\n",
      "(nbs, nue): (17, 118)\n",
      "(nbs, nue): (17, 119)\n",
      "(nbs, nue): (18, 116)\n",
      "(nbs, nue): (18, 117)\n",
      "(nbs, nue): (18, 118)\n",
      "(nbs, nue): (18, 119)\n",
      "(nbs, nue): (19, 116)\n",
      "(nbs, nue): (19, 117)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(nbs, nue): (19, 118)\n",
      "(nbs, nue): (19, 119)\n",
      "(nbs, nue): (20, 116)\n",
      "(nbs, nue): (20, 117)\n",
      "(nbs, nue): (20, 118)\n",
      "(nbs, nue): (20, 119)\n",
      "***********  [29]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [30]-Passed Through Channel *********** \n",
      "***********  [30]-Noise Added *********** \n",
      "***********  [30]-Resource Grid Extracted *********** \n",
      "UE-Index: 120 | slot-Index: 30\n",
      "UE-Index: 121 | slot-Index: 30\n",
      "UE-Index: 122 | slot-Index: 30\n",
      "UE-Index: 123 | slot-Index: 30\n",
      "***********  [30]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 120)\n",
      "(nbs, nue): (0, 121)\n",
      "(nbs, nue): (0, 122)\n",
      "(nbs, nue): (0, 123)\n",
      "(nbs, nue): (1, 120)\n",
      "(nbs, nue): (1, 121)\n",
      "(nbs, nue): (1, 122)\n",
      "(nbs, nue): (1, 123)\n",
      "(nbs, nue): (2, 120)\n",
      "(nbs, nue): (2, 121)\n",
      "(nbs, nue): (2, 122)\n",
      "(nbs, nue): (2, 123)\n",
      "(nbs, nue): (3, 120)\n",
      "(nbs, nue): (3, 121)\n",
      "(nbs, nue): (3, 122)\n",
      "(nbs, nue): (3, 123)\n",
      "(nbs, nue): (4, 120)\n",
      "(nbs, nue): (4, 121)\n",
      "(nbs, nue): (4, 122)\n",
      "(nbs, nue): (4, 123)\n",
      "(nbs, nue): (5, 120)\n",
      "(nbs, nue): (5, 121)\n",
      "(nbs, nue): (5, 122)\n",
      "(nbs, nue): (5, 123)\n",
      "(nbs, nue): (6, 120)\n",
      "(nbs, nue): (6, 121)\n",
      "(nbs, nue): (6, 122)\n",
      "(nbs, nue): (6, 123)\n",
      "(nbs, nue): (7, 120)\n",
      "(nbs, nue): (7, 121)\n",
      "(nbs, nue): (7, 122)\n",
      "(nbs, nue): (7, 123)\n",
      "(nbs, nue): (8, 120)\n",
      "(nbs, nue): (8, 121)\n",
      "(nbs, nue): (8, 122)\n",
      "(nbs, nue): (8, 123)\n",
      "(nbs, nue): (9, 120)\n",
      "(nbs, nue): (9, 121)\n",
      "(nbs, nue): (9, 122)\n",
      "(nbs, nue): (9, 123)\n",
      "(nbs, nue): (10, 120)\n",
      "(nbs, nue): (10, 121)\n",
      "(nbs, nue): (10, 122)\n",
      "(nbs, nue): (10, 123)\n",
      "(nbs, nue): (11, 120)\n",
      "(nbs, nue): (11, 121)\n",
      "(nbs, nue): (11, 122)\n",
      "(nbs, nue): (11, 123)\n",
      "(nbs, nue): (12, 120)\n",
      "(nbs, nue): (12, 121)\n",
      "(nbs, nue): (12, 122)\n",
      "(nbs, nue): (12, 123)\n",
      "(nbs, nue): (13, 120)\n",
      "(nbs, nue): (13, 121)\n",
      "(nbs, nue): (13, 122)\n",
      "(nbs, nue): (13, 123)\n",
      "(nbs, nue): (14, 120)\n",
      "(nbs, nue): (14, 121)\n",
      "(nbs, nue): (14, 122)\n",
      "(nbs, nue): (14, 123)\n",
      "(nbs, nue): (15, 120)\n",
      "(nbs, nue): (15, 121)\n",
      "(nbs, nue): (15, 122)\n",
      "(nbs, nue): (15, 123)\n",
      "(nbs, nue): (16, 120)\n",
      "(nbs, nue): (16, 121)\n",
      "(nbs, nue): (16, 122)\n",
      "(nbs, nue): (16, 123)\n",
      "(nbs, nue): (17, 120)\n",
      "(nbs, nue): (17, 121)\n",
      "(nbs, nue): (17, 122)\n",
      "(nbs, nue): (17, 123)\n",
      "(nbs, nue): (18, 120)\n",
      "(nbs, nue): (18, 121)\n",
      "(nbs, nue): (18, 122)\n",
      "(nbs, nue): (18, 123)\n",
      "(nbs, nue): (19, 120)\n",
      "(nbs, nue): (19, 121)\n",
      "(nbs, nue): (19, 122)\n",
      "(nbs, nue): (19, 123)\n",
      "(nbs, nue): (20, 120)\n",
      "(nbs, nue): (20, 121)\n",
      "(nbs, nue): (20, 122)\n",
      "(nbs, nue): (20, 123)\n",
      "***********  [30]-ToA Estimated *********** \n",
      "***********  SRS Grid Generated *********** \n",
      "***********  Transmission Grid Generated *********** \n",
      "***********  [31]-Passed Through Channel *********** \n",
      "***********  [31]-Noise Added *********** \n",
      "***********  [31]-Resource Grid Extracted *********** \n",
      "UE-Index: 124 | slot-Index: 31\n",
      "UE-Index: 125 | slot-Index: 31\n",
      "UE-Index: 126 | slot-Index: 31\n",
      "UE-Index: 127 | slot-Index: 31\n",
      "***********  [31]-Channel Estimated *********** \n",
      "(nbs, nue): (0, 124)\n",
      "(nbs, nue): (0, 125)\n",
      "(nbs, nue): (0, 126)\n",
      "(nbs, nue): (0, 127)\n",
      "(nbs, nue): (1, 124)\n",
      "(nbs, nue): (1, 125)\n",
      "(nbs, nue): (1, 126)\n",
      "(nbs, nue): (1, 127)\n",
      "(nbs, nue): (2, 124)\n",
      "(nbs, nue): (2, 125)\n",
      "(nbs, nue): (2, 126)\n",
      "(nbs, nue): (2, 127)\n",
      "(nbs, nue): (3, 124)\n",
      "(nbs, nue): (3, 125)\n",
      "(nbs, nue): (3, 126)\n",
      "(nbs, nue): (3, 127)\n",
      "(nbs, nue): (4, 124)\n",
      "(nbs, nue): (4, 125)\n",
      "(nbs, nue): (4, 126)\n",
      "(nbs, nue): (4, 127)\n",
      "(nbs, nue): (5, 124)\n",
      "(nbs, nue): (5, 125)\n",
      "(nbs, nue): (5, 126)\n",
      "(nbs, nue): (5, 127)\n",
      "(nbs, nue): (6, 124)\n",
      "(nbs, nue): (6, 125)\n",
      "(nbs, nue): (6, 126)\n",
      "(nbs, nue): (6, 127)\n",
      "(nbs, nue): (7, 124)\n",
      "(nbs, nue): (7, 125)\n",
      "(nbs, nue): (7, 126)\n",
      "(nbs, nue): (7, 127)\n",
      "(nbs, nue): (8, 124)\n",
      "(nbs, nue): (8, 125)\n",
      "(nbs, nue): (8, 126)\n",
      "(nbs, nue): (8, 127)\n",
      "(nbs, nue): (9, 124)\n",
      "(nbs, nue): (9, 125)\n",
      "(nbs, nue): (9, 126)\n",
      "(nbs, nue): (9, 127)\n",
      "(nbs, nue): (10, 124)\n",
      "(nbs, nue): (10, 125)\n",
      "(nbs, nue): (10, 126)\n",
      "(nbs, nue): (10, 127)\n",
      "(nbs, nue): (11, 124)\n",
      "(nbs, nue): (11, 125)\n",
      "(nbs, nue): (11, 126)\n",
      "(nbs, nue): (11, 127)\n",
      "(nbs, nue): (12, 124)\n",
      "(nbs, nue): (12, 125)\n",
      "(nbs, nue): (12, 126)\n",
      "(nbs, nue): (12, 127)\n",
      "(nbs, nue): (13, 124)\n",
      "(nbs, nue): (13, 125)\n",
      "(nbs, nue): (13, 126)\n",
      "(nbs, nue): (13, 127)\n",
      "(nbs, nue): (14, 124)\n",
      "(nbs, nue): (14, 125)\n",
      "(nbs, nue): (14, 126)\n",
      "(nbs, nue): (14, 127)\n",
      "(nbs, nue): (15, 124)\n",
      "(nbs, nue): (15, 125)\n",
      "(nbs, nue): (15, 126)\n",
      "(nbs, nue): (15, 127)\n",
      "(nbs, nue): (16, 124)\n",
      "(nbs, nue): (16, 125)\n",
      "(nbs, nue): (16, 126)\n",
      "(nbs, nue): (16, 127)\n",
      "(nbs, nue): (17, 124)\n",
      "(nbs, nue): (17, 125)\n",
      "(nbs, nue): (17, 126)\n",
      "(nbs, nue): (17, 127)\n",
      "(nbs, nue): (18, 124)\n",
      "(nbs, nue): (18, 125)\n",
      "(nbs, nue): (18, 126)\n",
      "(nbs, nue): (18, 127)\n",
      "(nbs, nue): (19, 124)\n",
      "(nbs, nue): (19, 125)\n",
      "(nbs, nue): (19, 126)\n",
      "(nbs, nue): (19, 127)\n",
      "(nbs, nue): (20, 124)\n",
      "(nbs, nue): (20, 125)\n",
      "(nbs, nue): (20, 126)\n",
      "(nbs, nue): (20, 127)\n",
      "***********  [31]-ToA Estimated *********** \n"
     ]
    }
   ],
   "source": [
    "print(\"***********  Transmission Grid Beamformed *********** \")\n",
    "numRepetition    = 1\n",
    "numSlotsPerFrame = np.int32(10*(15000/scs))\n",
    "numUEsPerSlot    = transmissionComb\n",
    "numSlots         = np.int32(np.ceil(nUEs*numRepetition/transmissionComb))\n",
    "frameIndices     = np.int32(np.floor(np.arange(numUEsPerSlot*numRepetition)/transmissionComb)%numSlotsPerFrame)\n",
    "slotIndices      = np.int32(np.floor(np.floor(np.arange(numUEsPerSlot*numRepetition)/transmissionComb)/numSlotsPerFrame))\n",
    "combOffset       = np.int32(np.arange(numUEsPerSlot))\n",
    "\n",
    "ToAe              = np.zeros((nBSs,nUEs))\n",
    "\n",
    "for ns in range(numSlots):\n",
    "    \n",
    "    ## SRS Grid Generation\n",
    "\n",
    "    srsGrid      = np.zeros((numUEsPerSlot, 14, numRBs*12), dtype=np.complex64)\n",
    "    srsObject    = np.empty((numUEsPerSlot), dtype=object)\n",
    "\n",
    "    for nue in range(numUEsPerSlot):\n",
    "        srsObject[nue]   = ResourceMapperSRS(nrofSRS_Ports, transmissionComb, nrofSymbols, startPosition,\n",
    "                                             repetitionFactor, nrOfCyclicShift, groupOrSequenceHopping, \n",
    "                                             sequenceId[nue], combOffset[nue], ns, frameIndices[nue], \n",
    "                                             resourceType, purpose, subcarrierSpacing)\n",
    "\n",
    "        srsGrid[nue] = srsObject[nue](bSRS, cSRS, bHop, freqScalingFactor, startRBIndex, \n",
    "                                          enableStartRBHopping, freqDomainShift, freqDomainPosition, \n",
    "                                          srsPeriodicityInSlots, srsOffsetInSlots, betaSRS, \n",
    "                                          resourceGridSizeinRBs)[0,0,0]\n",
    "\n",
    "    XGrid     = np.zeros((numUEsPerSlot, 14, Nfft), dtype=np.complex64)\n",
    "    bwpOffset = np.random.randint(Nfft-resourceGridSizeinRBs*12)\n",
    "\n",
    "    print(\"***********  SRS Grid Generated *********** \")\n",
    "    ## Load the resource grid to Transmission Grid\n",
    "    XGrid[...,bwpOffset:(bwpOffset+resourceGridSizeinRBs*12)] = srsGrid\n",
    "\n",
    "    print(\"***********  Transmission Grid Generated *********** \")\n",
    "\n",
    "    del srsGrid\n",
    "\n",
    "    ## Beamforming\n",
    "    # Beamforming angles\n",
    "    # Inter-element spacing in vertical and horizontal\n",
    "    Pt_dBm= 23\n",
    "    Pt    = 10**(0.1*(Pt_dBm-30))\n",
    "    lamda = 3*10**8/carrierFrequency\n",
    "    d     = 0.5/lamda\n",
    "    theta = 0\n",
    "    # Wt    = np.sqrt(Pt/Nt)*np.exp(1j*2*np.pi*d*np.cos(theta)/(lamda*Nt)*np.arange(0,Nt))\n",
    "    # Xf    = Wt.reshape(-1,1,1)*XGrid1\n",
    "    Xf    = (transmissionComb*Pt/Nr)*XGrid[..., np.newaxis].repeat(Nr, axis = -1)\n",
    "    del XGrid\n",
    "    \n",
    "    ueIndices     = np.arange(ns*numUEsPerSlot, (ns+1)*numUEsPerSlot)\n",
    "    ## Pass through channel\n",
    "    Yf = (Hf[:,:,ueIndices].transpose(1,2,0,3,5,4)@Xf[np.newaxis,...,np.newaxis]).sum(1)\n",
    "\n",
    "    print(\"***********  [\"+str(ns)+\"]-Passed Through Channel *********** \")\n",
    "\n",
    "    ## Add Noise\n",
    "    BoltzmanConst = 1.380649*(10**(-23))\n",
    "    temperature   = 300\n",
    "    noisePower    = BoltzmanConst*temperature*scs\n",
    "#     noisePower    = 0\n",
    "    kppm          = 0\n",
    "    fCFO          = kppm*(np.random.rand()-0.5)*carrierFrequency*(10**(-6)); # fCFO = CFO*subcarrierSpacing\n",
    "    CFO           = (fCFO/scs)/Nfft\n",
    "    ##Yf            = AddNoise(True)(Y, noisePower, CFO)\n",
    "\n",
    "    # Yf            = AddNoise(False)(Y, noisePower, 0)    #Added\n",
    "    Yf            = np.complex64(Yf + np.sqrt(0.5*noisePower)*(np.random.standard_normal(Yf.shape) + 1j*np.random.standard_normal(Yf.shape)))\n",
    "\n",
    "    print(\"***********  [\"+str(ns)+\"]-Noise Added *********** \")\n",
    "\n",
    "    ## Extract Resource Grid\n",
    "    rxGrid = Yf[...,bwpOffset:(bwpOffset+resourceGridSizeinRBs*12),:,0].transpose(0,3,1,2)\n",
    "\n",
    "    print(\"***********  [\"+str(ns)+\"]-Resource Grid Extracted *********** \")\n",
    "\n",
    "    ## Channel Estimation and Interpolation\n",
    "    Hfest  = np.zeros((nBSs, numUEsPerSlot, Nt, 14, rxGrid.shape[-1]), dtype = np.complex64)\n",
    "    chEST  = ChannelEstimationSRS()\n",
    "    chGrid = rxGrid.reshape(nBSs*Nt,14,-1)[:,np.newaxis,np.newaxis,np.newaxis]\n",
    "\n",
    "    interpolatorType = \"Linear\" # \"Spline\", \"Linear\", \"Cubic\"\n",
    "\n",
    "    for nue in range(numUEsPerSlot):\n",
    "        print(\"UE-Index: \"+str(ueIndices[nue])+\" | slot-Index: \"+str(ns))\n",
    "\n",
    "        Hfest[:,nue] = chEST(chGrid, srsObject[nue], interpolatorType)[:,0,0,0].reshape(nBSs,Nt,14,-1)\n",
    "\n",
    "    Hest = Hfest.sum(-2)/14\n",
    "\n",
    "    print(\"***********  [\"+str(ns)+\"]-Channel Estimated *********** \")\n",
    "\n",
    "    ## ToA Estimation\n",
    "    toaEstimation = ToAEstimation(\"ESPRIT\", Hest[0, 0].T.shape)\n",
    "    \n",
    "    \n",
    "    Lpath         = 2\n",
    "    for nbs in range(nBSs):\n",
    "        for nue in range(numUEsPerSlot):\n",
    "            print(\"(nbs, nue): (\"+str(nbs)+\", \"+str(ueIndices[nue])+\")\")\n",
    "            delayEstimates = np.sort(toaEstimation(Hest[nbs, nue].T,\n",
    "                                                   Lpath,\n",
    "                                                   subCarrierSpacing = scs))\n",
    "            delayEstimates = delayEstimates[delayEstimates > 0]\n",
    "            K = Lpath\n",
    "            while((delayEstimates.size==0) or (delayEstimates[0]<=0 and K < 12)):\n",
    "                K = K + 1\n",
    "                delayEstimates = np.sort(toaEstimation(Hest[nbs, nue].T,\n",
    "                                                       numberOfPath = K,\n",
    "                                                       subCarrierSpacing = scs))\n",
    "                delayEstimates = delayEstimates[delayEstimates > 0]\n",
    "            if(delayEstimates.size == 0):\n",
    "                ToAe[nbs, ueIndices[nue]] = 10**-9\n",
    "            else:\n",
    "                ToAe[nbs, ueIndices[nue]] = delayEstimates[0]\n",
    "\n",
    "    print(\"***********  [\"+str(ns)+\"]-ToA Estimated *********** \")    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1c6635d",
   "metadata": {},
   "source": [
    "## Position Estimation: Based on UL-ToA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "34fba159",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Position Estimation\n",
    "rxPosition  = simLayoutObj.UELocations\n",
    "txPosition  = simLayoutObj.BSLocations\n",
    "\n",
    "# Selection of k Most Accurate Measurements\n",
    "#################################################################   \n",
    "    \n",
    "k         = 4  # Select k-best measurements\n",
    "error     = (np.abs(ToAe-channel.delays[0,0,...,0])/channel.delays[0,0,...,0]) # Compute the ToA error in each measurement\n",
    "\n",
    "positionEstimate = LeastSquareTDoA()\n",
    "# positionEstimate = LeastSquareToA()\n",
    "\n",
    "# Position Estimation Object:\n",
    "    # Positioning based on: ToA\n",
    "    # Optimization Method: Least Square\n",
    "\n",
    "rxPositionEstimate = np.zeros((nUEs,2,3))\n",
    "rxStdEstimate      = np.zeros((nUEs))\n",
    "kBestIndices       = np.zeros((nUEs, k), dtype = np.int8)\n",
    "\n",
    "for nue in range(nUEs):\n",
    "    bsIndices    = np.argmin(error[..., nue].reshape(-1, 3), axis = -1)\n",
    "    siteIndices  = np.argsort(np.min(error[..., nue].reshape(-1, 3), -1))[0:k]\n",
    "    kBestIndices[nue] = siteIndices*nSectorsPerSite + bsIndices[siteIndices]\n",
    "\n",
    "    toa               = ToAe[kBestIndices[nue],nue]\n",
    "    tdoa              = toa[1::] - toa[0]\n",
    "    rxPositionEstimate[nue], rxStdEstimate[nue] = positionEstimate(txPosition[kBestIndices[nue]], tdoa=tdoa)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ffc0bb0",
   "metadata": {},
   "source": [
    "## Visualization of Estimated Position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4e937e4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#################################################################\n",
    "rxPosition  = simLayoutObj.UELocations\n",
    "txPosition  = simLayoutObj.BSLocations\n",
    "rangeEst_2D = np.sqrt(np.abs((ToAe*(3*10**8))**2 - (rxPosition[:,2].reshape(1,-1)-txPosition[:,2].reshape(-1,1))**2))\n",
    "\n",
    "# fig, ax = plt.subplots()\n",
    "fig, ax = simLayoutObj.display2DTopology(isEqualAspectRatio = True)\n",
    "colors  = [\"k\",\"m\",\"r\",\"b\",\"g\",\"y\",\"crimson\"]\n",
    "\n",
    "linestyle_tuple = ['solid', 'dotted', 'dashed', 'dashdot',\n",
    "                   (0, (5, 10)),                # 'loosely dashed'\n",
    "                   (0, (1, 10)),                # 'loosely dotted'\n",
    "                   (5, (10, 3)),                # 'long dash with offset'\n",
    "                   (0, (5, 1)),                 # 'densely dashed'\n",
    "                   (0, (3, 10, 1, 10)),         # 'loosely dashdotted'\n",
    "                   (0, (3, 5, 1, 5)),           # 'dashdotted'\n",
    "                   (0, (3, 1, 1, 1)),           # 'densely dashdotted'\n",
    "                   (0, (3, 5, 1, 5, 1, 5)),     # 'dashdotdotted'\n",
    "                   (0, (3, 10, 1, 10, 1, 10)),  # 'loosely dashdotdotted'\n",
    "                   (0, (3, 1, 1, 1, 1, 1))]     # 'densely dashdotdotted'\n",
    "\n",
    "for nbs in range(k):\n",
    "    for nue in range(nUEs):\n",
    "            circle1 = plt.Circle((txPosition[kBestIndices[nue, nbs], 0], txPosition[kBestIndices[nue, nbs], 1]), rangeEst_2D[kBestIndices[nue, nbs], nue],\n",
    "                                 color = colors[nue%7], lw = 0.35, ls = linestyle_tuple[nue%7], fill = False, zorder = 0)\n",
    "            ax.add_artist(circle1)\n",
    "\n",
    "ax.scatter(txPosition[:,0], txPosition[:,1], marker=\"P\", color=\"b\", edgecolors='white',\n",
    "           s = 125, label=\"Tx-Locations\", zorder = 3)\n",
    "ax.scatter(rxPositionEstimate[:,0,0], rxPositionEstimate[:,0,1], marker=\"o\", color=\"g\",\n",
    "           s = 75, label=\"Estimated Rx-Locations\", zorder = 1)\n",
    "ax.scatter(rxPosition[:,0], rxPosition[:,1], marker=\".\", color=\"r\", edgecolors='white',\n",
    "           s = 100, label=\"True Rx-Locations\", zorder = 2)\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xlabel(\"x-coordinates (m)\")\n",
    "ax.set_ylabel(\"y-coordinates (m)\")\n",
    "ax.set_title(\"Transmitter's Locations and Estimation Accuracy (True UE Location vs Estimated UE Locations)\")\n",
    "ax.set_xlim([-200, 200])\n",
    "ax.set_ylim([-200, 200])\n",
    "ax.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "223e7f26",
   "metadata": {},
   "source": [
    "## Performance Analysis of Positioning Error for ToA based method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "691f8c3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbins = nUEs\n",
    "xlimit = 5\n",
    "ylimit = 1\n",
    "posError3D = np.linalg.norm(rxPositionEstimate[:,0]-rxPosition, axis=1)\n",
    "posError3D = np.where(np.isnan(posError3D), 0, posError3D)\n",
    "posError2D = np.linalg.norm(rxPositionEstimate[:, 0,0:2]-rxPosition[:, 0:2], axis=1)\n",
    "\n",
    "# Horizontal Error\n",
    "count, bins_count = np.histogram(posError2D, bins = nbins, range = [0, xlimit])\n",
    "pdf = count/nUEs\n",
    "cdf = np.cumsum(pdf)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(bins_count[1:], cdf, label = \"2D Positioning Error\")\n",
    "\n",
    "\n",
    "# Vertical Error\n",
    "count, bins_count = np.histogram(posError3D, bins = nbins, range = [0, xlimit])\n",
    "pdf = count/nUEs\n",
    "cdf = np.cumsum(pdf)\n",
    "ax.plot(bins_count[1:], cdf, label = \"Vertical Positioning Error\")\n",
    "\n",
    "ax.set_xticks(np.linspace(0, xlimit, 11))\n",
    "ax.set_xticks(np.linspace(0, xlimit, 21), minor=True)\n",
    "ax.set_yticks(np.linspace(0, ylimit, 11))\n",
    "ax.set_yticks(np.linspace(0, ylimit, 21), minor=True)\n",
    "ax.set_xlabel(\"Positioning Error (m)\")\n",
    "ax.set_ylabel(\"CDF of Positioning Error\")\n",
    "ax.set_title(\"CDF of Positioning Error\")\n",
    "ax.axhline(y = 0.2, lw = 2, alpha = 1, linestyle = ':', color = \"crimson\", label = \"50%ile Positioning Error line\")\n",
    "ax.axhline(y = 2/3, lw = 2, alpha = 1, linestyle = ':', color = \"magenta\", label = \"66.6%ile Positioning Error line\")\n",
    "ax.axhline(y = 0.9, lw = 2, alpha = 1, linestyle = ':', color = \"royalblue\", label = \"90%ile Positioning Error line\")\n",
    "ax.axvline(x = 0.4, lw = 2, alpha = 1, linestyle = '-', color = \"midnightblue\", label = \"50 cm Positioning Accuracy line\")\n",
    "\n",
    "# Specify different settings for major and minor grids\n",
    "ax.grid(which = 'minor', alpha = 0.25, linestyle = '--')\n",
    "ax.grid(which = 'major', alpha = 1)\n",
    "\n",
    "ax.set_xlim([0,xlimit])\n",
    "ax.set_ylim([0,ylimit])\n",
    "ax.legend()\n",
    "plt.show()\n",
    "\n",
    "# # Code to save the Database\n",
    "# idx  = 0\n",
    "# flag = True\n",
    "# while(flag):\n",
    "#     filename = \"Databases/ULTDoA-\"+str([idx])+\".npz\"\n",
    "#     if(os.path.exists(filename)):\n",
    "#         idx  = idx + 1\n",
    "#     else:\n",
    "#         np.savez(filename, posError3D = posError3D, posError2D = posError2D, \n",
    "#                  rxPositionEstimate = rxPositionEstimate, rxPosition = rxPosition, \n",
    "#                  ToAe = ToAe, txPosition = txPosition, propTerrain = propTerrain, \n",
    "#                  carrierFrequency = carrierFrequency, scs = scs, Nfft = Nfft, \n",
    "#                  nBSs = nBSs, nUEs = nUEs, numRBs = numRBs, \n",
    "#                  bsArrayStructure  = np.array([1,1,2,2,1]), \n",
    "#                  ueArrayStructure  = np.array([1,1,8,4,1]))\n",
    "#         flag = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40a4bbf8",
   "metadata": {},
   "source": [
    "## Performance Analysis: For 2000 UEs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "123bb85e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# filename = \"Databases/ULTDoA-\"+str([0])+\".npz\"\n",
    "# ds       = np.load(filename)\n",
    "\n",
    "# posError3Da = ds[\"posError3D\"]\n",
    "# posError2Da = ds[\"posError2D\"]\n",
    "\n",
    "# for i in range(1,idx+1):\n",
    "    \n",
    "#     filename   = \"Databases/ULTDoA-\"+str([i])+\".npz\"\n",
    "#     ds         = np.load(filename)\n",
    "    \n",
    "#     posError3Da = np.concatenate([posError3Da, ds[\"posError3D\"]])\n",
    "#     posError2Da = np.concatenate([posError2Da, ds[\"posError2D\"]])\n",
    "\n",
    "\n",
    "ds       = np.load(\"Databases/ULTDoA.npz\")\n",
    "posError3Da = ds[\"posError3D\"]\n",
    "posError2Da = ds[\"posError2D\"]\n",
    "\n",
    "nbins = posError2Da.size\n",
    "xlimit = 5\n",
    "ylimit = 1\n",
    "\n",
    "# Horizontal Error\n",
    "count, bins_count = np.histogram(posError2Da, bins = nbins, range = [0, xlimit])\n",
    "pdf = count/nbins\n",
    "cdf = np.cumsum(pdf)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(bins_count[1:], cdf, label = \"2D Positioning Error\")\n",
    "\n",
    "\n",
    "\n",
    "# Vertical Error\n",
    "count, bins_count = np.histogram(posError3Da, bins = nbins, range = [0, xlimit])\n",
    "pdf = count/nbins\n",
    "cdf = np.cumsum(pdf)\n",
    "ax.plot(bins_count[1:], cdf, label = \"Vertical Positioning Error\")\n",
    "\n",
    "ax.set_xticks(np.linspace(0, xlimit, 11))\n",
    "ax.set_xticks(np.linspace(0, xlimit, 21), minor=True)\n",
    "ax.set_yticks(np.linspace(0, ylimit, 11))\n",
    "ax.set_yticks(np.linspace(0, ylimit, 21), minor=True)\n",
    "ax.set_xlabel(\"Positioning Error (m)\")\n",
    "ax.set_ylabel(\"CDF of Positioning Error\")\n",
    "ax.set_title(\"CDF of Positioning Error\")\n",
    "ax.axhline(y = 0.2, lw = 2, alpha = 1, linestyle = ':', color = \"crimson\", label = \"50%ile Positioning Error line\")\n",
    "ax.axhline(y = 2/3, lw = 2, alpha = 1, linestyle = ':', color = \"magenta\", label = \"66.6%ile Positioning Error line\")\n",
    "ax.axhline(y = 0.9, lw = 2, alpha = 1, linestyle = ':', color = \"royalblue\", label = \"90%ile Positioning Error line\")\n",
    "ax.axvline(x = 1.5, lw = 2, alpha = 1, linestyle = '-', color = \"midnightblue\", label = \"1.5 m Positioning Accuracy line\")\n",
    "\n",
    "# Specify different settings for major and minor grids\n",
    "ax.grid(which = 'minor', alpha = 0.25, linestyle = '--')\n",
    "ax.grid(which = 'major', alpha = 1)\n",
    "\n",
    "ax.set_xlim([0,xlimit])\n",
    "ax.set_ylim([0,ylimit])\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0464917d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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