examples/5G_NR_PUSCH.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d021b529",
   "metadata": {},
   "source": [
    "# 5G NR PUSCH Tutorial\n",
    "\n",
    "This notebook provides an introduction to Sionna's [5G New Radio (NR) module](https://nvlabs.github.io/sionna/api/nr.html) and, in particular, the [physical uplink shared channel (PUSCH)](https://nvlabs.github.io/sionna/api/nr.html#pusch). This module provides implementations of a small subset of the physical layer functionalities as described in the 3GPP specifications [38.211](https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3213), [38.212](https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3214) and [38.214](https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3216). \n",
    "\n",
    "\n",
    "You will\n",
    "\n",
    "- Get an understanding of the different components of a PUSCH configuration, such as the carrier, DMRS, and transport block,\n",
    "- Learn how to rapidly simulate PUSCH transmissions for multiple transmitters,\n",
    "- Modify the PUSCHReceiver to use a custom MIMO Detector.\n",
    "\n",
    "## Table of Contents\n",
    "* [GPU Configuration and Imports](#GPU-Configuration-and-Imports)\n",
    "* [A \"Hello World!\" Example](#A-Hello-World-Example)\n",
    "* [Carrier Configuration](#Carrier-Configuration)\n",
    "* [Understanding the DMRS Configuration](#Understanding-the-DMRS-Configuration)\n",
    "    * [Configuring Multiple Layers](#Configuring-Multiple-Layers)\n",
    "    * [Controlling the Number of DMRS Symbols in a Slot](#Controlling-the-Number-of-DMRS-Symbols-in-a-Slot)\n",
    "    * [How to control the number of available DMRS ports?](#How-to-control-the-number-of-available-DMRS-ports?)\n",
    "* [Transport Blocks and MCS](#Transport-Blocks-and-MCS)\n",
    "* [Looking into the PUSCHTransmitter](#Looking-into-the-PUSCHTransmitter)\n",
    "* [Components of the PUSCHReceiver](#Components-of-the-PUSCHReceiver)\n",
    "* [End-to-end PUSCH Simulations](#End-to-end-PUSCH-Simulations)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3174011",
   "metadata": {
    "tags": []
   },
   "source": [
    "## GPU Configuration and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "376e6700",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ea5ac108",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of GPUs available : 2\n",
      "Only GPU number 0 used.\n"
     ]
    }
   ],
   "source": [
    "# Configure the notebook to use only a single GPU and allocate only as much memory as needed\n",
    "# For more details, see https://www.tensorflow.org/guide/gpu\n",
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "print('Number of GPUs available :', len(gpus))\n",
    "if gpus:\n",
    "    gpu_num = 0 # Number of the GPU to be used\n",
    "    try:\n",
    "        #tf.config.set_visible_devices([], 'GPU')\n",
    "        tf.config.set_visible_devices(gpus[gpu_num], 'GPU')\n",
    "        print('Only GPU number', gpu_num, 'used.')\n",
    "        tf.config.experimental.set_memory_growth(gpus[gpu_num], True)\n",
    "    except RuntimeError as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20d7cf1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Sionna\n",
    "try:\n",
    "    import sionna\n",
    "except ImportError as e:\n",
    "    # Install Sionna if package is not already installed\n",
    "    import os\n",
    "    os.system(\"pip install sionna\")\n",
    "    import sionna\n",
    "\n",
    "# Load the required Sionna components\n",
    "from sionna.nr import PUSCHConfig, PUSCHTransmitter, PUSCHReceiver\n",
    "from sionna.channel import AWGN, RayleighBlockFading, OFDMChannel, TimeChannel, time_lag_discrete_time_channel\n",
    "from sionna.channel.tr38901 import AntennaArray, UMi, UMa, RMa\n",
    "from sionna.channel import gen_single_sector_topology as gen_topology\n",
    "from sionna.utils import compute_ber, ebnodb2no, sim_ber\n",
    "from sionna.ofdm import KBestDetector, LinearDetector\n",
    "from sionna.mimo import StreamManagement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74cdcae8",
   "metadata": {
    "tags": []
   },
   "source": [
    "## A Hello World Example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70cfc8d3",
   "metadata": {},
   "source": [
    "Let us start with a simple \"Hello, World!\" example in which we will simulate PUSCH transmissions from a single transmitter to a single receiver over an AWGN channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "726ccf12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BER: 0.0\n"
     ]
    }
   ],
   "source": [
    "# Create a PUSCH configuration with default settings\n",
    "pusch_config = PUSCHConfig()\n",
    "\n",
    "# Instantiate a PUSCHTransmitter from the PUSCHConfig\n",
    "pusch_transmitter = PUSCHTransmitter(pusch_config)\n",
    "\n",
    "# Create a PUSCHReceiver using the PUSCHTransmitter\n",
    "pusch_receiver = PUSCHReceiver(pusch_transmitter)\n",
    "\n",
    "# AWGN channel\n",
    "channel = AWGN()\n",
    "\n",
    "# Simulate transmissions over the AWGN channel\n",
    "batch_size = 16\n",
    "no = 0.1 # Noise variance\n",
    "\n",
    "x, b = pusch_transmitter(batch_size) # Generate transmit signal and info bits\n",
    "\n",
    "y = channel([x, no]) # Simulate channel output\n",
    "\n",
    "b_hat = pusch_receiver([x, no]) # Recover the info bits\n",
    "\n",
    "# Compute BER\n",
    "print(\"BER:\", compute_ber(b, b_hat).numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e34c2f8",
   "metadata": {},
   "source": [
    "Although the above code snippet seems rather simple, you have actually carried out standard-compliant simulations of the NR PUSCH!\n",
    "\n",
    "To better understand what is actually going on under the hood, we can inspect the OFDM resource grid that is generated by the transmitter with the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "71e70335",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pusch_transmitter.resource_grid.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "812933e2",
   "metadata": {},
   "source": [
    "The above figure tells us that we are simulating a slot of 14 OFDM symbols spanning 48 subcarriers, which correspond to four physical resource blocks (PRBs) in 5G terminology. The third OFDM symbol is reserved for pilot transmissions, so-called demodulation reference signals (DMRS), and the rest is used for data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bad481f",
   "metadata": {},
   "source": [
    "## Carrier Configuration\n",
    "\n",
    "When you create a PUSCHConfig instance, it automatically creates a CarrierConfig instance with default settings.\n",
    "You can inspect this configuration with the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c010bd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Carrier Configuration\n",
      "=====================\n",
      "cyclic_prefix : normal\n",
      "cyclic_prefix_length : 5.208333333333334e-06\n",
      "frame_duration : 0.01\n",
      "frame_number : 0\n",
      "kappa : 64.0\n",
      "mu : 0\n",
      "n_cell_id : 1\n",
      "n_size_grid : 4\n",
      "n_start_grid : 0\n",
      "num_slots_per_frame : 10\n",
      "num_slots_per_subframe : 1\n",
      "num_symbols_per_slot : 14\n",
      "slot_number : 0\n",
      "sub_frame_duration : 0.001\n",
      "subcarrier_spacing : 15\n",
      "t_c : 5.086263020833334e-10\n",
      "t_s : 3.2552083333333335e-08\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pusch_config.carrier.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "443ffd6a",
   "metadata": {},
   "source": [
    "Most of these parameters cannot be controlled as they are simply derived from others. For example, the cyclic prefix length depends on the subcarrier spacing.\n",
    "Let us see what happens, when we choose larger subcarrier spacing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8ca703b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Carrier Configuration\n",
      "=====================\n",
      "cyclic_prefix : normal\n",
      "cyclic_prefix_length : 1.6927083333333335e-06\n",
      "frame_duration : 0.01\n",
      "frame_number : 0\n",
      "kappa : 64.0\n",
      "mu : 2\n",
      "n_cell_id : 1\n",
      "n_size_grid : 4\n",
      "n_start_grid : 0\n",
      "num_slots_per_frame : 40\n",
      "num_slots_per_subframe : 4\n",
      "num_symbols_per_slot : 14\n",
      "slot_number : 0\n",
      "sub_frame_duration : 0.001\n",
      "subcarrier_spacing : 60\n",
      "t_c : 5.086263020833334e-10\n",
      "t_s : 3.2552083333333335e-08\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pusch_config.carrier.subcarrier_spacing = 60\n",
    "pusch_config.carrier.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd7dfcb",
   "metadata": {},
   "source": [
    "The cyclic prefix has shrunk from $5.2 \\mu s$ to $1.69 \\mu s$ and the number of slots per frame has increased from $10$ to $40$.\n",
    "\n",
    "If we change to the extended cyclic prefix, the number of OFDM symbols per slot will decrease from 14 to 12."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3316b591",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Carrier Configuration\n",
      "=====================\n",
      "cyclic_prefix : extended\n",
      "cyclic_prefix_length : 4.166666666666667e-06\n",
      "frame_duration : 0.01\n",
      "frame_number : 0\n",
      "kappa : 64.0\n",
      "mu : 2\n",
      "n_cell_id : 1\n",
      "n_size_grid : 4\n",
      "n_start_grid : 0\n",
      "num_slots_per_frame : 40\n",
      "num_slots_per_subframe : 4\n",
      "num_symbols_per_slot : 12\n",
      "slot_number : 0\n",
      "sub_frame_duration : 0.001\n",
      "subcarrier_spacing : 60\n",
      "t_c : 5.086263020833334e-10\n",
      "t_s : 3.2552083333333335e-08\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pusch_config_ext = pusch_config.clone()\n",
    "pusch_config_ext.carrier.cyclic_prefix = \"extended\"\n",
    "pusch_config_ext.carrier.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e1803b5",
   "metadata": {},
   "source": [
    "Please have a look at the API documentation of [PUSCHCarrierConfig](https://nvlabs.github.io/sionna/api/nr.html#sionna.nr.CarrierConfig) for more detail."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "390851a7",
   "metadata": {},
   "source": [
    "## Understanding the DMRS Configuration\n",
    "\n",
    "We can learn more about the structure of the resoure grid by having a look at the pilot pattern in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "541d253b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pusch_transmitter.pilot_pattern.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96a9afc5",
   "metadata": {},
   "source": [
    "From the figure above, we can see that there is a single transmitter sending a single stream (or so-called layer).\n",
    "DMRS are only sent on even subcarriers while odd subcarriers are masked, i.e., blocked for data transmission.\n",
    "This corresponds to the DMRS Configuration Type 1 with the parameter `NumCDMGroupsWithoutData` set to 2. We will explain what that means later.\n",
    "\n",
    "In 5G NR, one can configure many different pilot patterns to adapt to different channel conditions and to allow for spatial multiplexing of up to twelve layers. Each transmitted layer is identified by a DMRS port, i.e., a distinct pilot pattern. In our running example, the transmitter uses the DMRS port 0.\n",
    "\n",
    "With the current PUSCH configuration, four different DMRS ports 0,1,2,3 are available.\n",
    "This can be verified with the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8310da11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pusch_config.dmrs.allowed_dmrs_ports"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09b846c4",
   "metadata": {},
   "source": [
    "Next, we configure three other transmitters using each one of the remaing ports. Then, we create a new PUSCHTransmitter instance from the list of PUSCH configurations which is able to generate transmit signals for all four transmitters in parallel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fb66df3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x: (16, 4, 1, 14, 48)\n"
     ]
    }
   ],
   "source": [
    "# Clone the original PUSCHConfig and change the DMRS port set\n",
    "pusch_config_1 = pusch_config.clone()\n",
    "pusch_config_1.dmrs.dmrs_port_set = [1]\n",
    "pusch_config_2 = pusch_config.clone()\n",
    "pusch_config_2.dmrs.dmrs_port_set = [2]\n",
    "pusch_config_3 = pusch_config.clone()\n",
    "pusch_config_3.dmrs.dmrs_port_set = [3]\n",
    "\n",
    "# Create a PUSCHTransmitter from the list of PUSCHConfigs\n",
    "pusch_transmitter_multi = PUSCHTransmitter([pusch_config, pusch_config_1, pusch_config_2, pusch_config_3])\n",
    "\n",
    "# Generate a batch of random transmit signals\n",
    "x, b  = pusch_transmitter_multi(batch_size)\n",
    "\n",
    "# x has shape [batch_size, num_tx, num_tx_ant, num_ofdm_symbols, fft_size]\n",
    "print(\"Shape of x:\", x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd19d2c6",
   "metadata": {},
   "source": [
    "Inspecting the shape of x reveals that we have indeed four single-antenna transmitters. Let us now have a look at the resuling pilot pattern for each of them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "80feec0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pusch_transmitter_multi.pilot_pattern.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "313111da",
   "metadata": {},
   "source": [
    "As before, all transmitters send pilots only on the third OFDM symbol. Transmitter 0 and 1 (using DMRS port 0 and 1, respectively) send pilots on all even subcarriers, while Transmitter 2 and 3 (using DMRS port 2 and 3, respectively), send pilots on the odd subcarriers.\n",
    "This means that the pilots signals of DMRS port 0 and 1 (as well as 2 and 3) interfere with each other as they occupy the same resource elements.\n",
    "So how can we estimate the channel coefficients for both transmitters individually without pilot contamination? \n",
    "\n",
    "The solution to this problem are the so-called code division multiplexing (CDM) groups in 5G NR.\n",
    "DMRS ports 0,1 belong to CDM group 0, while DMRS ports 2,3 belong to CDM group 1.\n",
    "\n",
    "The pilot signals belonging to the same CDM group are multiplied by orthogonal cover codes which allow separating them during channel estimation.\n",
    "The way this works is as follows. Denote by $\\mathbf{p_0} = [s_1, s_2]^\\textsf{T}$ a pair of two adjacent pilot symbols, e.g., those on subcarrier 0 and 2, of DMRS port 0. DMRS port 1 will simply send $\\mathbf{p_1} = [s_1, -s_2]^\\textsf{T}$. If we assume that the channel is constant over both subcarriers, we get the following received pilot signal at the receiver (we look only at a single antenna here):\n",
    "\n",
    "\\begin{align}\n",
    "\\mathbf{y} = h_0\\mathbf{p}_0 + h_1\\mathbf{p}_1 + \\mathbf{n}\n",
    "\\end{align}\n",
    "\n",
    "where $\\mathbf{y}\\in\\mathbb{C}^2$ is the received signal on both subcarriers, $h_0, h_1$ are the channel coefficients for both users, and $\\mathbf{n}\\in\\mathbb{C}^2$ is a noise vector.\n",
    "\n",
    "We can now obtain channel estimates for both transmitters by projecting $\\mathbf{y}$ onto their respective pilot sequences:\n",
    "\n",
    "\\begin{align}\n",
    "\\hat{h}_0 &= \\frac{\\mathbf{p}_0^\\mathsf{H}}{\\lVert \\mathbf{p}_0 \\rVert|^2} \\mathbf{y} = h_0 + \\frac{|s_1|^2-|s_2|^2}{\\lVert \\mathbf{p}_0 \\rVert|^2} h_1 + \\frac{\\mathbf{p}_0^\\mathsf{H}}{\\lVert \\mathbf{p}_0 \\rVert|^2} \\mathbf{n} = h_0 + n_0 \\\\\n",
    "\\hat{h}_1 &= \\frac{\\mathbf{p}_1^\\mathsf{H}}{\\lVert \\mathbf{p}_1 \\rVert|^2} \\mathbf{y} = \\frac{|s_1|^2-|s_2|^2}{\\lVert \\mathbf{p}_1 \\rVert|^2} h_0 + h_1 +\\frac{\\mathbf{p}_1^\\mathsf{H}}{\\lVert \\mathbf{p}_1 \\rVert|^2} \\mathbf{n} = h_1 + n_1.\n",
    "\\end{align}\n",
    "\n",
    "Since the pilot symbols have the same amplitude, we have $|s_1|^2-|s_2|^2=0$, i.e., the interference between both pilot sequence is zero. Moreover, due to an implict averaging of the channel estimates for both subcarriers, the effective noise variance is reduced by a factor of 3dB since\n",
    "\n",
    "\\begin{align}\n",
    "\\mathbb{E}\\left[ |n_0|^2 \\right] = \\mathbb{E}\\left[ |n_1|^2 \\right] = \\frac{\\sigma^2}{\\lVert \\mathbf{p}_1 \\rVert|^2} = \\frac{\\sigma^2}{2 |s_0|^2}.\n",
    "\\end{align}\n",
    "\n",
    "We can access the actual pilot sequences that are transmitted as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2884bdf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of pilots: (4, 1, 48)\n"
     ]
    }
   ],
   "source": [
    "# pilots has shape [num_tx, num_layers, num_pilots]\n",
    "pilots = pusch_transmitter_multi.pilot_pattern.pilots\n",
    "print(\"Shape of pilots:\", pilots.shape)\n",
    "\n",
    "# Select only the non-zero subcarriers for all sequence\n",
    "p_0 = pilots[0,0,::2] # Pilot sequence of TX 0 on even subcarriers\n",
    "p_1 = pilots[1,0,::2] # Pilot sequence of TX 1 on even subcarriers\n",
    "p_2 = pilots[2,0,1::2] # Pilot sequence of TX 2 on odd subcarriers\n",
    "p_3 = pilots[3,0,1::2] # Pilot sequence of TX 3 on odd subcarriers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75de0579",
   "metadata": {},
   "source": [
    "Each pilot pattern consists of 48 symbols that are transmitted on the third OFDM symbol with 4PRBs, i.e., 48 subcarriers. \n",
    "Let us now verify that pairs of two adjacent pilot symbols in `p_0` and `p_1` as well as in `p_2` and `p_3` are orthogonal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b3fbc970",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j\n",
      " 0.+0.j 0.+0.j]\n",
      "[0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j 0.+0.j\n",
      " 0.+0.j 0.+0.j]\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(np.reshape(p_0, [-1,2]) * np.reshape(np.conj(p_1), [-1,2]), axis=1))\n",
    "print(np.sum(np.reshape(p_2, [-1,2]) * np.reshape(np.conj(p_3), [-1,2]), axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34942413",
   "metadata": {},
   "source": [
    "Let us now come back to the masked resource elements in each pilot pattern. \n",
    "The parameter `NumCDMGroupsWithoutData` mentioned earlier determines which resource elements in a DMRS-carrying OFDM symbol are masked for data transmissions. This is to avoid inference with pilots from other DMRS groups.\n",
    "\n",
    "In our example, `NumCDMGroupsWithoutData` is set to two. This means that no data can be transmitted on any of the resource elements occupied by both DMRS groups. However, if we would have set `NumCDMGroupsWithoutData` equal to one, data and pilots would be frequency multiplexed. \n",
    "This can be useful, if we only schedule transmissions from DMRS ports in the same CDM group.\n",
    "\n",
    "Here is an example of such a configuration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7d16844f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pusch_config = PUSCHConfig()\n",
    "pusch_config.dmrs.num_cdm_groups_without_data = 1\n",
    "pusch_config.dmrs.dmrs_port_set = [0]\n",
    "\n",
    "pusch_config_1 = pusch_config.clone()\n",
    "pusch_config_1.dmrs.dmrs_port_set = [1]\n",
    "\n",
    "PUSCHTransmitter([pusch_config, pusch_config_1]).pilot_pattern.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98221864",
   "metadata": {},
   "source": [
    "The DRMS ports 0 and 1 belong both to CDM group 0 so that the resource elements of CDM group 1 do not need to be masked and can be used for data transmission. One can see in the above figure that data and pilots are now indeed multiplexed in the frequency domain."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c329e4a5",
   "metadata": {},
   "source": [
    "### Configuring Multiple Layers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfd3c2a9",
   "metadata": {},
   "source": [
    "In 5G NR, a transmitter can be equipped with 1,2, or 4 antenna ports, i.e., physical antennas that are fed with an individual transmit signal.\n",
    "It can transmit 1,2,3 or 4 layers, i.e., spatial streams, as long as the number of layers does not exceed the number of antenna ports. \n",
    "Using codebook-based precoding, a number of layers can be mapped onto a larger number of antenna ports, e.g., 2 layers using 4 antenna ports. If no precoding is used, each layer is simply mapped to one of the antenna ports.\n",
    "\n",
    "It is important to understand that each layer is transmitted using a different DMRS port. That means that the number of DMRS ports is independent of the number of antenna ports.\n",
    "\n",
    "In the next cell, we will configure a single transmitter with four antenna ports, sending two layers on DMRS ports 0 and 1.\n",
    "We can then choose among different precoding matrices with the help of the transmit transmit precoding matrix identifier (TPMI). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c624eeb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5+0.j , 0. +0.j ],\n",
       "       [0. +0.j , 0.5+0.j ],\n",
       "       [0.5+0.j , 0. +0.j ],\n",
       "       [0. +0.j , 0. +0.5j]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pusch_config = PUSCHConfig()\n",
    "pusch_config.num_antenna_ports = 4\n",
    "pusch_config.num_layers = 2\n",
    "pusch_config.dmrs.dmrs_port_set = [0,1]\n",
    "pusch_config.precoding = \"codebook\"\n",
    "pusch_config.tpmi = 7\n",
    "\n",
    "# Show the precoding matrix\n",
    "pusch_config.precoding_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "26516c5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PUSCHTransmitter(pusch_config).pilot_pattern.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a84a7981",
   "metadata": {},
   "source": [
    "We can see from the pilot patterns above, that we have now a single transmitter sending two streams. Both streams will be precoded and transmit over four antenna ports. From a channel estimation perspective at the receiver, however, this scenario is identical to the previous one with two single-antenna transmitters. The receiver will simply estimate the effective channel (including precoding) for every configured DMRS port."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fba3ecaa",
   "metadata": {},
   "source": [
    "### Controlling the Number of DMRS Symbols in a Slot\n",
    "\n",
    "How can we add additional DMRS symbols to the resource grid to enable channel estimation for high-speed scenarios? \n",
    "\n",
    "This can be controlled with the parameter ``DMRS.additional_position``.\n",
    "In the next cell, we configure one additional DMRS symbol to the pattern and visualize it. You can try setting it to different values and see the impact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4f6fe409",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pusch_config.dmrs.additional_position = 1\n",
    "\n",
    "# In order to reduce the number of figures, we only limit us here \n",
    "# to the pilot pattern of the first stream\n",
    "PUSCHTransmitter(pusch_config).pilot_pattern.show(stream_ind = 0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa57e45d",
   "metadata": {},
   "source": [
    "### How to control the number of available DMRS ports?\n",
    "\n",
    "There are two factors that determine the available number of DMRS ports, i.e., layers, that can be transmitted. \n",
    "The first is the DMRS Configuration and the second the length of a DMRS symbol. Both parameters can take to values so that there are four options in total.\n",
    "In the previous example, the DMRS Configuration Type 1 was used. In this case, there are two CDM groups and each groups uses either odd or even subcarriers. This leads to four available DMRS ports.\n",
    "With DMRS Configuration Type 2, there are three CDM groups and each group uses two pairs of adjacent subcarriers per PRB, i.e., four pilot-carrying subcarriers. That means that there are six available DMRS ports.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cfe0fcb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available DMRS ports: [0, 1, 2, 3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pusch_config.dmrs.config_type = 2\n",
    "PUSCHTransmitter(pusch_config).pilot_pattern.show(stream_ind = 0);\n",
    "print(\"Available DMRS ports:\", pusch_config.dmrs.allowed_dmrs_ports)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e7b8a25",
   "metadata": {},
   "source": [
    "In the above figure, you can see that the pilot pattern has become sparser in the frequency domain. However, there are still only four available DMRS ports. This is because we now need to mask also the resource elements that are used by the third CDM group. This can be done by setting the parameter `NumCDMGroupsWithoutData` equal to three."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e98b5c61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available antenna ports: [0, 1, 2, 3, 4, 5]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pusch_config.dmrs.num_cdm_groups_without_data = 3\n",
    "PUSCHTransmitter(pusch_config).pilot_pattern.show(stream_ind = 0);\n",
    "print(\"Available antenna ports:\", pusch_config.dmrs.allowed_dmrs_ports)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d723977",
   "metadata": {},
   "source": [
    "The second parameter that controls the number of available DMRS ports is the ``length``, which can be equal to either one or two.\n",
    "Let's see what happens when we change it to two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cbc1719f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available DMRS ports: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pusch_config.n_size_bwp = 1 # We reduce the bandwidth to one PRB for better visualization\n",
    "pusch_config.dmrs.length = 2\n",
    "PUSCHTransmitter(pusch_config).pilot_pattern.show(stream_ind = 0);\n",
    "print(\"Available DMRS ports:\", pusch_config.dmrs.allowed_dmrs_ports)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fe17ece",
   "metadata": {},
   "source": [
    "The pilot pattern is now composed of four 2x2 blocks within a PRB. These blocks are used by the four DMRS ports within the same CDM group. This means that we can now support up to twelve layers! \n",
    "\n",
    "Let's create a setup with three transmitters, each sending four layers using four antenna ports. We choose the DMRS ports for each transmitters such that they belong to the CDM group. This is not necessary and you are free to choose any desired allocation. \n",
    "It is however important to understand, thet for channel estimation to work, the channel is supposed to be static over 2x2 blocks of resource elements. This is in general the case for low mobility scenarios and channels with not too large delay spread. You can see from the results below that the pilot sequences of the DMRS ports in the same CDM group are indeed orthogonal over the 2x2 blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d936f636",
   "metadata": {},
   "outputs": [],
   "source": [
    "pusch_config = PUSCHConfig()\n",
    "pusch_config.n_size_bwp = 1\n",
    "pusch_config.dmrs.config_type = 2\n",
    "pusch_config.dmrs.length = 2\n",
    "pusch_config.dmrs.additional_position = 1\n",
    "pusch_config.dmrs.num_cdm_groups_without_data = 3\n",
    "pusch_config.num_antenna_ports = 4\n",
    "pusch_config.num_layers = 4\n",
    "pusch_config.dmrs.dmrs_port_set = [0,1,6,7]\n",
    "pusch_config.precoding = \"codebook\"\n",
    "pusch_config.tpmi = 4\n",
    "\n",
    "pusch_config_1 = pusch_config.clone()\n",
    "pusch_config_1.dmrs.dmrs_port_set = [2,3,8,9]\n",
    "pusch_config_2 = pusch_config.clone()\n",
    "pusch_config_2.dmrs.dmrs_port_set = [4,5,10,11]\n",
    "\n",
    "pusch_transmitter_multi = PUSCHTransmitter([pusch_config, pusch_config_1, pusch_config_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c1ef3b92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ True, False, False, False],\n",
       "        [False,  True, False, False],\n",
       "        [False, False,  True, False],\n",
       "        [False, False, False,  True]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the first 2x2 block of pilot symbols for all DMRS ports of the first transmitter\n",
    "p = pusch_transmitter_multi.pilot_pattern.pilots[0].numpy()\n",
    "p = np.matrix(p[:, [0,1,12,13]])\n",
    "\n",
    "# Test that these pilot sequences are mutually orthogonal\n",
    "# The result should be a boolean identity matrix\n",
    "np.abs(p*p.getH())>1e-6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c31350c2",
   "metadata": {},
   "source": [
    "There are several other parameters that impact the pilot patterns. The full DMRS configuration can be displayed with the following command.\n",
    "We refer to the [API documentation of the PUSCHDMRSConfig class](https://nvlabs.github.io/sionna/api/nr.html#puschdmrsconfig) for further details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "372e465c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PUSCH DMRS Configuration\n",
      "========================\n",
      "additional_position : 1\n",
      "allowed_dmrs_ports : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n",
      "beta : 1.7320508075688772\n",
      "cdm_groups : [0, 0, 0, 0]\n",
      "config_type : 2\n",
      "deltas : [0, 0, 0, 0]\n",
      "dmrs_port_set : [0, 1, 6, 7]\n",
      "length : 2\n",
      "n_id : None\n",
      "n_scid : 0\n",
      "num_cdm_groups_without_data : 3\n",
      "type_a_position : 2\n",
      "w_f : [[ 1  1  1  1]\n",
      " [ 1 -1  1 -1]]\n",
      "w_t : [[ 1  1  1  1]\n",
      " [ 1  1 -1 -1]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pusch_config.dmrs.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddc22dc4",
   "metadata": {},
   "source": [
    "## Transport Blocks and MCS\n",
    "\n",
    "The modulation and coding scheme (MCS) is set in 5G NR via the MCS index and MCS table which are properties of transport block configuration [TBConfig](https://nvlabs.github.io/sionna/api/nr.html#sionna.nr.TBConfig). When you create an instance of ``PUSCHConfig``, a default instance of ``TBConfig`` is created. It can be accessed via the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8c08f4e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transport Block Configuration\n",
      "=============================\n",
      "channel_type : PUSCH\n",
      "mcs_index : 14\n",
      "mcs_table : 1\n",
      "n_id : None\n",
      "num_bits_per_symbol : 4\n",
      "target_coderate : 0.5400390625\n",
      "tb_scaling : 1.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pusch_config = PUSCHConfig()\n",
    "pusch_config.tb.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0d2301c",
   "metadata": {},
   "source": [
    "You can see that the current MCS Table is 1 and the MCS index is 14. Looking at the corresponding table in the API documentation of [TBConfig](https://nvlabs.github.io/sionna/api/nr.html#sionna.nr.TBConfig), you can see that we should have  a 16QAM modulation (i.e., 4 bits per symbol) and a target coderate of 553/1024=0.54 which matches the values above. The data scrambling ID $n_\\text{ID}$ is set to `None` which implies that the physical layer cell id $N^\\text{cell}_\\text{ID}$ will be used instead.\n",
    "\n",
    "We can change the MCS index and table as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2d06067d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transport Block Configuration\n",
      "=============================\n",
      "channel_type : PUSCH\n",
      "mcs_index : 26\n",
      "mcs_table : 2\n",
      "n_id : None\n",
      "num_bits_per_symbol : 8\n",
      "target_coderate : 0.89501953125\n",
      "tb_scaling : 1.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pusch_config.tb.mcs_index = 26\n",
    "pusch_config.tb.mcs_table = 2\n",
    "pusch_config.tb.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac404602",
   "metadata": {},
   "source": [
    "The transport block segmentation allows the PUSCH transmitter to fill resource grids of almost arbitrary size and with any of the possible DMRS configurations.\n",
    "The number of information bits transmitted in a single slot is given by the property ``tb_size`` of the ``PUSCHConfig``.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1eb1b886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303240"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding more PRBs will increase the TB size\n",
    "pusch_config.carrier.n_size_grid = 273\n",
    "pusch_config.tb_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a0b5e407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1213032"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding more layers will increase the TB size\n",
    "pusch_config.num_antenna_ports = 4\n",
    "pusch_config.num_layers = 4\n",
    "pusch_config.tb_size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a07cff",
   "metadata": {},
   "source": [
    "For more details about how the transportblock encoding/decoding works, we refer to the API documentation of the [TBEncoder](https://nvlabs.github.io/sionna/api/nr.html#sionna.nr.TBEncoder)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8f838a3",
   "metadata": {},
   "source": [
    "## Looking into the PUSCHTransmitter\n",
    "\n",
    "We have used the ``PUSCHTransmitter`` class already multiple times without speaking about what it actually does. In short, it generates for every configured transmitter a batch of random information bits of length ``pusch_config.tb_size`` and outputs either a frequency fo time-domain representation of the transmitted OFDM waveform from each of the antenna ports of each transmitter.\n",
    "\n",
    "However, under the hood it implements the sequence of layers shown in the following figure: "
   ]
  },
  {
   "attachments": {
    "45dafb96-b379-4430-82c8-9df578084676.jpg": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "0ddd20f7",
   "metadata": {},
   "source": [
    "![Picture 1.jpg](attachment:45dafb96-b379-4430-82c8-9df578084676.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd633a17",
   "metadata": {},
   "source": [
    "Information bits are either randomly generated or provided as input and then encoded into a transport block by the [TBEncoder](https://nvlabs.github.io/sionna/api/nr.html#sionna.nr.TBEncoder). The encoded bits are then mapped to QAM constellation symbols by the [Mapper](https://nvlabs.github.io/sionna/api/mapping.html#sionna.mapping.Mapper). The [LayerMapper](https://nvlabs.github.io/sionna/api/nr.html#sionna.nr.LayerMapper) splits the modulated symbols into different layers which are then mapped onto OFDM resource grids by the [ResourceGridMapper](https://nvlabs.github.io/sionna/api/ofdm.html#sionna.ofdm.ResourceGridMapper). If precoding is enabled in the [PUSCHConfig](https://nvlabs.github.io/sionna/api/nr.html#sionna.nr.PUSCHConfig), the resource grids are further precoded by the [PUSCHPrecoder](https://nvlabs.github.io/sionna/api/nr.html#sionna.nr.PUSCHPrecoder) so that there is one for each transmitter and antenna port. If ``output_domain`` equals “freq”, these are the ouputs \n",
    "x\n",
    ". If ``output_domain`` is chosen to be “time”, the resource grids are transformed into time-domain signals by the [OFDMModulator](https://nvlabs.github.io/sionna/api/ofdm.html#sionna.ofdm.OFDMModulator)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac65ef08",
   "metadata": {},
   "source": [
    "Let us configure a ``PUSCHTransmitter`` from a list of two ``PUSCHConfig`` and inspect the output shapes: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "15ee99f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of b: (32, 2, 2728)\n",
      "Shape of x: (32, 2, 4, 14, 48)\n"
     ]
    }
   ],
   "source": [
    "pusch_config = PUSCHConfig()\n",
    "pusch_config.num_antenna_ports = 4\n",
    "pusch_config.num_layers = 2\n",
    "pusch_config.dmrs.dmrs_port_set = [0,1]\n",
    "pusch_config.precoding = \"codebook\"\n",
    "pusch_config.tpmi = 7\n",
    "\n",
    "pusch_config_1 = pusch_config.clone()\n",
    "pusch_config.dmrs.dmrs_port_set = [2,3]\n",
    "\n",
    "pusch_transmitter = PUSCHTransmitter([pusch_config, pusch_config_1])\n",
    "\n",
    "batch_size = 32\n",
    "x, b = pusch_transmitter(batch_size)\n",
    "\n",
    "# b has shape [batch_size, num_tx, tb_size]\n",
    "print(\"Shape of b:\", b.shape)\n",
    "\n",
    "# x has shape [batch_size, num_tx, num_tx_ant, num_ofdm_symbols, num_subcarriers]\n",
    "print(\"Shape of x:\", x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f492ecd",
   "metadata": {},
   "source": [
    "If you want to transmit a custom payload, you simply need to deactive the ``return_bits`` flag when creating the transmitter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3d6e5ba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pusch_transmitter = PUSCHTransmitter([pusch_config, pusch_config_1], return_bits=False)\n",
    "x_2 = pusch_transmitter(b)\n",
    "assert np.array_equal(x, x_2) # Check that we get the same output for the payload b generated above"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69052c60",
   "metadata": {},
   "source": [
    "By default, the ``PUSCHTransmitter`` generates frequency-domain outputs. If you want to make time-domain simulations, you need to configure the ``output_domain`` during the initialization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "601b1e56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of x: (32, 2, 4, 728)\n"
     ]
    }
   ],
   "source": [
    "pusch_transmitter = PUSCHTransmitter([pusch_config, pusch_config_1], output_domain=\"time\", return_bits=False)\n",
    "x_time = pusch_transmitter(b)\n",
    "\n",
    "# x has shape [batch_size, num_tx, num_tx_ant, num_time_samples]\n",
    "print(\"Shape of x:\", x_time.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07f11907",
   "metadata": {},
   "source": [
    "The last dimension of the output signal correspond to the total number of time-domain samples which can be computed in the following way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2f4c25f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "728"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pusch_transmitter.resource_grid.cyclic_prefix_length  \\\n",
    " + pusch_transmitter.resource_grid.fft_size) \\\n",
    "* pusch_transmitter.resource_grid.num_ofdm_symbols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f050ea6",
   "metadata": {},
   "source": [
    "## Components of the PUSCHReceiver\n",
    "\n",
    "The [PUSCHReceiver](https://nvlabs.github.io/sionna/api/nr.html#sionna.nr.PUSCHReceiver) is the counter-part to the [PUSCHTransmitter](https://nvlabs.github.io/sionna/api/nr.html#sionna.nr.PUSCHTransmitter) as it *simply* recovers the transmitted information bits from received waveform. It combines multiple processing blocks in a single layer as shown in the following figure:"
   ]
  },
  {
   "attachments": {
    "f7ad545c-20f0-47d6-8f02-6473dce4f964.jpg": {
     "image/jpeg": 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"
    }
   },
   "cell_type": "markdown",
   "id": "ebd40350",
   "metadata": {},
   "source": [
    "![Picture 2.jpg](attachment:f7ad545c-20f0-47d6-8f02-6473dce4f964.jpg)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a01b5609",
   "metadata": {},
   "source": [
    "If the ``input_domain`` equals “time”, the inputs $\\mathbf{y}$\n",
    " are first transformed to resource grids with the [OFDMDemodulator](https://nvlabs.github.io/sionna/api/ofdm.html#sionna.ofdm.OFDMModulator). Then channel estimation is performed, e.g., with the help of the [PUSCHLSChannelEstimator](https://nvlabs.github.io/sionna/api/nr.html#sionna.nr.PUSCHLSChannelEstimator). If ``channel_estimator`` is chosen to be “perfect”, this step is skipped and the input $\\mathbf{h}$ is used instead. Next, MIMO detection is carried out with an arbitrary [OFDMDetector](https://nvlabs.github.io/sionna/api/ofdm.html#sionna.ofdm.OFDMDetector). The resulting LLRs for each layer are then combined to transport blocks with the help of the [LayerDemapper](https://nvlabs.github.io/sionna/api/nr.html#sionna.nr.LayerDemapper). Finally, the transport blocks are decoded with the [TBDecoder](https://nvlabs.github.io/sionna/api/nr.html#sionna.nr.TBDecoder).\n",
    " \n",
    "If we instantiate a ``PUSCHReceiver`` as done in the next cell, default implementations of all blocks as described in the [API documentation](https://nvlabs.github.io/sionna/api/nr.html#sionna.nr.PUSCHReceiver) are used.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0e2c8f36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sionna.ofdm.detection.LinearDetector at 0x7f86f28c72b0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pusch_receiver = PUSCHReceiver(pusch_transmitter)\n",
    "pusch_receiver._mimo_detector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48501379",
   "metadata": {},
   "source": [
    "We can also provide custom implementations for each block by providing them as keyword arguments during initialization.\n",
    "In the folllwing code snippet, we first create an instance of the [KBestDetector](https://nvlabs.github.io/sionna/api/ofdm.html#sionna.ofdm.KBestDetector), which is then used as MIMO detector in the ``PUSCHReceiver``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "66c43c7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new PUSCHTransmitter\n",
    "pusch_transmitter = PUSCHTransmitter([pusch_config, pusch_config_1])\n",
    "\n",
    "# Create a StreamManagement instance\n",
    "rx_tx_association = np.ones([1, pusch_transmitter.resource_grid.num_tx], bool)\n",
    "stream_management = StreamManagement(rx_tx_association,\n",
    "                                     pusch_config.num_layers)\n",
    "\n",
    "# Get relevant parameters for the detector\n",
    "num_streams = pusch_transmitter.resource_grid.num_tx \\\n",
    "              * pusch_transmitter.resource_grid.num_streams_per_tx\n",
    "\n",
    "k = 32 # Number of canditates for K-Best detection \n",
    "\n",
    "k_best = KBestDetector(\"bit\", num_streams, k,\n",
    "                       pusch_transmitter.resource_grid,\n",
    "                       stream_management,\n",
    "                       \"qam\", pusch_config.tb.num_bits_per_symbol)\n",
    "\n",
    "# Create a PUSCHReceiver using the KBest detector\n",
    "pusch_receiver = PUSCHReceiver(pusch_transmitter, mimo_detector=k_best)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5decf6b8",
   "metadata": {},
   "source": [
    "Next, we test if this receiver works over a simple Rayleigh block fading channel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0ec8ce0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BER: 0.0\n"
     ]
    }
   ],
   "source": [
    "num_rx_ant = 16\n",
    "rayleigh = RayleighBlockFading(num_rx=1,\n",
    "                               num_rx_ant=num_rx_ant,\n",
    "                               num_tx=pusch_transmitter.resource_grid.num_tx,\n",
    "                               num_tx_ant=pusch_config.num_antenna_ports)\n",
    "\n",
    "channel = OFDMChannel(rayleigh,\n",
    "                      pusch_transmitter.resource_grid,\n",
    "                      add_awgn=True,\n",
    "                      normalize_channel=True)\n",
    "\n",
    "x, b = pusch_transmitter(32)\n",
    "no = 0.1\n",
    "y = channel([x, no])\n",
    "b_hat = pusch_receiver([y, no])\n",
    "print(\"BER:\", compute_ber(b, b_hat).numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86bebb7b",
   "metadata": {},
   "source": [
    "## End-to-end PUSCH Simulations\n",
    "\n",
    "We will now implement a end-to-end Keras model that is capable of running PUSCH simulations for many different configurations.\n",
    "You can use it as a boilerplate template for your own experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "eb2425de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need to enable sionna.config.xla_compat before we can use\n",
    "# tf.function with jit_compile=True.\n",
    "# See https://nvlabs.github.io/sionna/api/config.html#sionna.Config.xla_compat\n",
    "sionna.config.xla_compat=True\n",
    "\n",
    "class Model(tf.keras.Model):\n",
    "    \"\"\"Simulate PUSCH transmissions over a 3GPP 38.901 model.\n",
    "    \n",
    "    This model runs BER simulations for a multiuser MIMO uplink channel\n",
    "    compliant with the 5G NR PUSCH specifications. \n",
    "    You can pick different scenarios, i.e., channel models, perfect or\n",
    "    estimated CSI, as well as different MIMO detectors (LMMSE or KBest).\n",
    "    You can chosse to run simulations in either time (\"time\") or frequency (\"freq\")\n",
    "    domains and configure different user speeds.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    scenario : str, one of [\"umi\", \"uma\", \"rma\"]\n",
    "        3GPP 38.901 channel model to be used\n",
    "        \n",
    "    perfect_csi : bool\n",
    "        Determines if perfect CSI is assumed or if the CSI is estimated\n",
    "        \n",
    "    domain :  str, one of [\"freq\", \"time\"]\n",
    "        Domain in which the simulations are carried out.\n",
    "        Time domain modelling is typically more complex but allows modelling\n",
    "        of realistic effects such as inter-symbol interference of subcarrier\n",
    "        interference due to very high speeds.\n",
    "    \n",
    "    detector : str, one of [\"lmmse\", \"kbest\"]\n",
    "        MIMO detector to be used. Note that each detector has additional\n",
    "        parameters that can be configured in the source code of the _init_ call.\n",
    "        \n",
    "    speed: float\n",
    "        User speed (m/s)\n",
    "        \n",
    "    Input\n",
    "    -----\n",
    "    batch_size : int\n",
    "        Number of simultaneously simulated slots\n",
    "        \n",
    "    ebno_db : float\n",
    "        Signal-to-noise-ratio\n",
    "    \n",
    "    Output\n",
    "    ------\n",
    "    b : [batch_size, num_tx, tb_size], tf.float\n",
    "        Transmitted information bits\n",
    "        \n",
    "    b_hat : [batch_size, num_tx, tb_size], tf.float\n",
    "        Decoded information bits   \n",
    "    \"\"\"\n",
    "    def __init__(self,\n",
    "                 scenario,    # \"umi\", \"uma\", \"rma\"\n",
    "                 perfect_csi, # bool\n",
    "                 domain,      # \"freq\", \"time\"\n",
    "                 detector,    # \"lmmse\", \"kbest\"\n",
    "                 speed        # float\n",
    "                ):  \n",
    "        super().__init__()\n",
    "        self._scenario = scenario\n",
    "        self._perfect_csi = perfect_csi\n",
    "        self._domain = domain\n",
    "        self._speed = speed\n",
    "        \n",
    "        self._carrier_frequency = 3.5e9\n",
    "        self._subcarrier_spacing = 30e3\n",
    "        self._num_tx = 4\n",
    "        self._num_tx_ant = 4\n",
    "        self._num_layers = 2\n",
    "        self._num_rx_ant = 16\n",
    "        self._mcs_index = 14\n",
    "        self._mcs_table = 1\n",
    "        self._num_prb = 16\n",
    "        \n",
    "        # Create PUSCHConfigs\n",
    "        \n",
    "        # PUSCHConfig for the first transmitter\n",
    "        pusch_config = PUSCHConfig()\n",
    "        pusch_config.carrier.subcarrier_spacing = self._subcarrier_spacing/1000\n",
    "        pusch_config.carrier.n_size_grid = self._num_prb\n",
    "        pusch_config.num_antenna_ports = self._num_tx_ant\n",
    "        pusch_config.num_layers = self._num_layers\n",
    "        pusch_config.precoding = \"codebook\"\n",
    "        pusch_config.tpmi = 1\n",
    "        pusch_config.dmrs.dmrs_port_set = list(range(self._num_layers))\n",
    "        pusch_config.dmrs.config_type = 2\n",
    "        pusch_config.dmrs.length = 2\n",
    "        pusch_config.dmrs.additional_position = 1\n",
    "        pusch_config.dmrs.num_cdm_groups_without_data = 3\n",
    "        pusch_config.tb.mcs_index = self._mcs_index\n",
    "        pusch_config.tb.mcs_table = self._mcs_table\n",
    "        \n",
    "        # Create PUSCHConfigs for the other transmitters by cloning of the first PUSCHConfig\n",
    "        # and modifying the used DMRS ports.\n",
    "        pusch_configs = [pusch_config]\n",
    "        for i in range(1, self._num_tx):\n",
    "            pc = pusch_config.clone()\n",
    "            pc.dmrs.dmrs_port_set = list(range(i*self._num_layers, (i+1)*self._num_layers))\n",
    "            pusch_configs.append(pc)\n",
    "            \n",
    "        # Create PUSCHTransmitter\n",
    "        self._pusch_transmitter = PUSCHTransmitter(pusch_configs, output_domain=self._domain)\n",
    "        \n",
    "        # Create PUSCHReceiver\n",
    "        self._l_min, self._l_max = time_lag_discrete_time_channel(self._pusch_transmitter.resource_grid.bandwidth)\n",
    "        \n",
    "        \n",
    "        rx_tx_association = np.ones([1, self._num_tx], bool)\n",
    "        stream_management = StreamManagement(rx_tx_association,\n",
    "                                             self._num_layers)\n",
    "        \n",
    "        assert detector in[\"lmmse\", \"kbest\"], \"Unsupported MIMO detector\"\n",
    "        if detector==\"lmmse\":\n",
    "            detector = LinearDetector(equalizer=\"lmmse\",\n",
    "                                      output=\"bit\",\n",
    "                                      demapping_method=\"maxlog\",\n",
    "                                      resource_grid=self._pusch_transmitter.resource_grid,\n",
    "                                      stream_management=stream_management,\n",
    "                                      constellation_type=\"qam\",\n",
    "                                      num_bits_per_symbol=pusch_config.tb.num_bits_per_symbol)\n",
    "        elif detector==\"kbest\":\n",
    "            detector = KBestDetector(output=\"bit\",\n",
    "                                     num_streams=self._num_tx*self._num_layers,\n",
    "                                     k=64,\n",
    "                                     resource_grid=self._pusch_transmitter.resource_grid,\n",
    "                                     stream_management=stream_management,\n",
    "                                     constellation_type=\"qam\",\n",
    "                                     num_bits_per_symbol=pusch_config.tb.num_bits_per_symbol)\n",
    "            \n",
    "        if self._perfect_csi:\n",
    "            self._pusch_receiver = PUSCHReceiver(self._pusch_transmitter,\n",
    "                                                 mimo_detector=detector,\n",
    "                                                 input_domain=self._domain,\n",
    "                                                 channel_estimator=\"perfect\",\n",
    "                                                 l_min = self._l_min)\n",
    "        else:\n",
    "            self._pusch_receiver = PUSCHReceiver(self._pusch_transmitter,\n",
    "                                                 mimo_detector=detector,\n",
    "                                                 input_domain=self._domain,\n",
    "                                                 l_min = self._l_min)\n",
    "        \n",
    "        # Configure antenna arrays\n",
    "        self._ut_array = AntennaArray(\n",
    "                                 num_rows=1,\n",
    "                                 num_cols=int(self._num_tx_ant/2),\n",
    "                                 polarization=\"dual\",\n",
    "                                 polarization_type=\"cross\",\n",
    "                                 antenna_pattern=\"omni\",\n",
    "                                 carrier_frequency=self._carrier_frequency)\n",
    "\n",
    "        self._bs_array = AntennaArray(num_rows=1,\n",
    "                                      num_cols=int(self._num_rx_ant/2),\n",
    "                                      polarization=\"dual\",\n",
    "                                      polarization_type=\"cross\",\n",
    "                                      antenna_pattern=\"38.901\",\n",
    "                                      carrier_frequency=self._carrier_frequency)\n",
    "            \n",
    "        # Configure the channel model\n",
    "        if self._scenario == \"umi\":\n",
    "            self._channel_model = UMi(carrier_frequency=self._carrier_frequency,\n",
    "                                      o2i_model=\"low\",\n",
    "                                      ut_array=self._ut_array,\n",
    "                                      bs_array=self._bs_array,\n",
    "                                      direction=\"uplink\",\n",
    "                                      enable_pathloss=False,\n",
    "                                      enable_shadow_fading=False)\n",
    "        elif self._scenario == \"uma\":\n",
    "            self._channel_model = UMa(carrier_frequency=self._carrier_frequency,\n",
    "                                      o2i_model=\"low\",\n",
    "                                      ut_array=self._ut_array,\n",
    "                                      bs_array=self._bs_array,\n",
    "                                      direction=\"uplink\",\n",
    "                                      enable_pathloss=False,\n",
    "                                      enable_shadow_fading=False)\n",
    "        elif self._scenario == \"rma\":\n",
    "            self._channel_model = RMa(carrier_frequency=self._carrier_frequency,\n",
    "                                      ut_array=self._ut_array,\n",
    "                                      bs_array=self._bs_array,\n",
    "                                      direction=\"uplink\",\n",
    "                                      enable_pathloss=False,\n",
    "                                      enable_shadow_fading=False)\n",
    "            \n",
    "        # Configure the actual channel\n",
    "        if domain==\"freq\":\n",
    "            self._channel = OFDMChannel(\n",
    "                                self._channel_model,\n",
    "                                self._pusch_transmitter.resource_grid,\n",
    "                                normalize_channel=True,\n",
    "                                return_channel=True)\n",
    "        else:\n",
    "            self._channel = TimeChannel(\n",
    "                                self._channel_model,\n",
    "                                self._pusch_transmitter.resource_grid.bandwidth,\n",
    "                                self._pusch_transmitter.resource_grid.num_time_samples,\n",
    "                                l_min=self._l_min,\n",
    "                                l_max=self._l_max,\n",
    "                                normalize_channel=True,\n",
    "                                return_channel=True)\n",
    "            \n",
    "    def new_topology(self, batch_size):\n",
    "        \"\"\"Set new topology\"\"\"\n",
    "        topology = gen_topology(batch_size,\n",
    "                                self._num_tx,\n",
    "                                self._scenario,\n",
    "                                min_ut_velocity=self._speed,\n",
    "                                max_ut_velocity=self._speed)\n",
    "\n",
    "        self._channel_model.set_topology(*topology)\n",
    "            \n",
    "    @tf.function(jit_compile=True)\n",
    "    def call(self, batch_size, ebno_db):\n",
    "        self.new_topology(batch_size)\n",
    "        x, b = self._pusch_transmitter(batch_size)\n",
    "        no = ebnodb2no(ebno_db,\n",
    "                       self._pusch_transmitter._num_bits_per_symbol,\n",
    "                       pusch_transmitter._target_coderate,\n",
    "                       pusch_transmitter.resource_grid)     \n",
    "        y, h = self._channel([x, no])\n",
    "        if self._perfect_csi:\n",
    "            b_hat = self._pusch_receiver([y, h, no])\n",
    "        else:\n",
    "            b_hat = self._pusch_receiver([y, no])\n",
    "        return b, b_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d18f8840",
   "metadata": {},
   "source": [
    "We will now compare the PUSCH BLER performance over the 3GPP 38.901 UMi channel model with different detectors and either perfect or imperfect CSI.\n",
    "Note that these simulations might take some time depending or you available hardware. You can reduce the `batch_size` if the model does not fit into the memory of your GPU. Running the simulations in the time domain will significantly increase the complexity and you might need to decrease the `batch_size` further. The code will also run on CPU if not GPU is available. \n",
    "\n",
    "Note that the XLA compilation step can take several minutes (but the simulations will be much quicker compared to eager or graph mode.\n",
    "\n",
    "If you do not want to run the simulation yourself, you can skip the next cell and visualize the results in the next cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ddbb4e6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
      "Instructions for updating:\n",
      "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -2.0 | 8.4141e-02 | 8.6523e-01 |      352915 |     4194304 |          443 |         512 |       174.7 |reached target block errors\n",
      "     -1.0 | 3.8089e-02 | 5.2539e-01 |      159757 |     4194304 |          269 |         512 |         0.6 |reached target block errors\n",
      "      0.0 | 1.3623e-02 | 2.3633e-01 |      114277 |     8388608 |          242 |        1024 |         1.2 |reached target block errors\n",
      "      1.0 | 5.1620e-03 | 7.0312e-02 |      129906 |    25165824 |          216 |        3072 |         3.5 |reached target block errors\n",
      "      2.0 | 1.8941e-03 | 2.2895e-02 |      142999 |    75497472 |          211 |        9216 |        10.6 |reached target block errors\n",
      "      3.0 | 6.8813e-04 | 8.1787e-03 |      138539 |   201326592 |          201 |       24576 |        28.4 |reached target block errors\n",
      "      4.0 | 2.8596e-04 | 3.1917e-03 |      147528 |   515899392 |          201 |       62976 |        73.4 |reached target block errors\n",
      "      5.0 | 1.2890e-04 | 1.1800e-03 |      181657 |  1409286144 |          203 |      172032 |       201.4 |reached target block errors\n",
      "      6.0 | 9.3746e-05 | 7.2742e-04 |      211149 |  2252341248 |          200 |      274944 |       321.9 |reached target block errors\n",
      "      7.0 | 3.3643e-05 | 2.9883e-04 |      141111 |  4194304000 |          153 |      512000 |       599.8 |reached max iter       \n",
      "      8.0 | 2.6884e-05 | 1.8945e-04 |      112758 |  4194304000 |           97 |      512000 |       599.8 |reached max iter       \n",
      "      9.0 | 1.2900e-05 | 1.0742e-04 |       54107 |  4194304000 |           55 |      512000 |       599.8 |reached max iter       \n",
      "     10.0 | 6.6764e-06 | 4.6875e-05 |       28003 |  4194304000 |           24 |      512000 |       599.8 |reached max iter       \n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -2.0 | 3.2366e-02 | 5.0195e-01 |      135754 |     4194304 |          257 |         512 |       171.7 |reached target block errors\n",
      "     -1.0 | 1.5961e-02 | 2.4121e-01 |      133888 |     8388608 |          247 |        1024 |         1.0 |reached target block errors\n",
      "      0.0 | 9.8741e-03 | 1.3411e-01 |      124245 |    12582912 |          206 |        1536 |         1.4 |reached target block errors\n",
      "      1.0 | 4.3543e-03 | 6.5569e-02 |      127843 |    29360128 |          235 |        3584 |         3.4 |reached target block errors\n",
      "      2.0 | 2.7020e-03 | 3.7109e-02 |      124664 |    46137344 |          209 |        5632 |         5.2 |reached target block errors\n",
      "      3.0 | 1.5692e-03 | 2.1073e-02 |      125056 |    79691776 |          205 |        9728 |         9.3 |reached target block errors\n",
      "      4.0 | 8.9329e-04 | 1.2251e-02 |      123642 |   138412032 |          207 |       16896 |        16.1 |reached target block errors\n",
      "      5.0 | 5.4429e-04 | 7.2443e-03 |      125561 |   230686720 |          204 |       28160 |        26.8 |reached target block errors\n",
      "      6.0 | 2.9038e-04 | 3.8869e-03 |      123013 |   423624704 |          201 |       51712 |        48.7 |reached target block errors\n",
      "      7.0 | 2.1879e-04 | 2.7646e-03 |      130307 |   595591168 |          201 |       72704 |        69.2 |reached target block errors\n",
      "      8.0 | 1.3987e-04 | 1.5751e-03 |      145486 |  1040187392 |          200 |      126976 |       121.0 |reached target block errors\n",
      "      9.0 | 7.8782e-05 | 9.8387e-04 |      132505 |  1681915904 |          202 |      205312 |       195.2 |reached target block errors\n",
      "     10.0 | 6.4391e-05 | 7.2391e-04 |      147192 |  2285895680 |          202 |      279040 |       266.3 |reached target block errors\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -2.0 | 1.5518e-01 | 9.9414e-01 |      650858 |     4194304 |          509 |         512 |        43.2 |reached target block errors\n",
      "     -1.0 | 1.2702e-01 | 9.7266e-01 |      532773 |     4194304 |          498 |         512 |         0.6 |reached target block errors\n",
      "      0.0 | 8.9072e-02 | 8.4961e-01 |      373596 |     4194304 |          435 |         512 |         0.6 |reached target block errors\n",
      "      1.0 | 3.6323e-02 | 5.1367e-01 |      152349 |     4194304 |          263 |         512 |         0.6 |reached target block errors\n",
      "      2.0 | 1.5565e-02 | 2.3438e-01 |      130566 |     8388608 |          240 |        1024 |         1.2 |reached target block errors\n",
      "      3.0 | 8.8474e-03 | 1.1523e-01 |      148435 |    16777216 |          236 |        2048 |         2.3 |reached target block errors\n",
      "      4.0 | 5.3303e-03 | 5.1270e-02 |      178854 |    33554432 |          210 |        4096 |         4.7 |reached target block errors\n",
      "      5.0 | 3.5277e-03 | 2.8878e-02 |      207146 |    58720256 |          207 |        7168 |         8.2 |reached target block errors\n",
      "      6.0 | 2.9088e-03 | 2.3093e-02 |      207410 |    71303168 |          201 |        8704 |        10.0 |reached target block errors\n",
      "      7.0 | 2.5939e-03 | 1.8694e-02 |      228468 |    88080384 |          201 |       10752 |        12.4 |reached target block errors\n",
      "      8.0 | 2.0388e-03 | 1.3672e-02 |      247983 |   121634816 |          203 |       14848 |        17.2 |reached target block errors\n",
      "      9.0 | 1.7823e-03 | 1.2451e-02 |      239211 |   134217728 |          204 |       16384 |        19.0 |reached target block errors\n",
      "     10.0 | 1.9271e-03 | 1.3739e-02 |      234399 |   121634816 |          204 |       14848 |        17.2 |reached target block errors\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "     -2.0 | 1.0344e-01 | 9.1992e-01 |      433850 |     4194304 |          471 |         512 |        43.9 |reached target block errors\n",
      "     -1.0 | 6.6115e-02 | 7.2461e-01 |      277305 |     4194304 |          371 |         512 |         0.5 |reached target block errors\n",
      "      0.0 | 4.3680e-02 | 5.3320e-01 |      183209 |     4194304 |          273 |         512 |         0.5 |reached target block errors\n",
      "      1.0 | 2.1767e-02 | 2.8027e-01 |      182593 |     8388608 |          287 |        1024 |         1.0 |reached target block errors\n",
      "      2.0 | 1.3199e-02 | 1.6536e-01 |      166083 |    12582912 |          254 |        1536 |         1.4 |reached target block errors\n",
      "      3.0 | 7.3069e-03 | 8.9844e-02 |      153236 |    20971520 |          230 |        2560 |         2.4 |reached target block errors\n",
      "      4.0 | 5.2081e-03 | 5.6920e-02 |      152911 |    29360128 |          204 |        3584 |         3.4 |reached target block errors\n",
      "      5.0 | 4.0943e-03 | 4.3620e-02 |      154555 |    37748736 |          201 |        4608 |         4.3 |reached target block errors\n",
      "      6.0 | 3.9949e-03 | 3.5006e-02 |      217828 |    54525952 |          233 |        6656 |         6.2 |reached target block errors\n",
      "      7.0 | 2.3833e-03 | 2.0559e-02 |      189928 |    79691776 |          200 |        9728 |         9.1 |reached target block errors\n",
      "      8.0 | 2.3061e-03 | 1.7578e-02 |      222467 |    96468992 |          207 |       11776 |        11.1 |reached target block errors\n",
      "      9.0 | 2.3562e-03 | 1.8466e-02 |      217420 |    92274688 |          208 |       11264 |        10.6 |reached target block errors\n",
      "     10.0 | 2.1590e-03 | 1.5325e-02 |      235439 |   109051904 |          204 |       13312 |        12.6 |reached target block errors\n"
     ]
    }
   ],
   "source": [
    "PUSCH_SIMS = {\n",
    "    \"scenario\" : [\"umi\"],\n",
    "    \"domain\" : [\"freq\"],\n",
    "    \"perfect_csi\" : [True, False],\n",
    "    \"detector\" : [\"kbest\", \"lmmse\"],\n",
    "    \"ebno_db\" : list(range(-2,11)),\n",
    "    \"speed\" : 3.0,\n",
    "    \"batch_size_freq\" : 128,\n",
    "    \"batch_size_time\" : 28, # Reduced batch size from time-domain modeling\n",
    "    \"bler\" : [],\n",
    "    \"ber\" : []\n",
    "    }\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "for scenario in PUSCH_SIMS[\"scenario\"]:\n",
    "    for domain in PUSCH_SIMS[\"domain\"]:\n",
    "        for perfect_csi in PUSCH_SIMS[\"perfect_csi\"]:\n",
    "            batch_size = PUSCH_SIMS[\"batch_size_freq\"] if domain==\"freq\" else PUSCH_SIMS[\"batch_size_time\"]\n",
    "            for detector in PUSCH_SIMS[\"detector\"]:\n",
    "                model = Model(scenario, perfect_csi, domain, detector, PUSCH_SIMS[\"speed\"])\n",
    "                ber, bler = sim_ber(model,\n",
    "                            PUSCH_SIMS[\"ebno_db\"],\n",
    "                            batch_size=batch_size,\n",
    "                            max_mc_iter=1000,\n",
    "                            num_target_block_errors=200)\n",
    "                PUSCH_SIMS[\"ber\"].append(list(ber.numpy()))\n",
    "                PUSCH_SIMS[\"bler\"].append(list(bler.numpy()))\n",
    "                \n",
    "PUSCH_SIMS[\"duration\"] = time.time() - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c247baed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation duration: 1.22 [h]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load results (un-comment to show saved results from the cell above)\n",
    "PUSCH_SIMS = eval(\"{'scenario': ['umi'], 'domain': ['freq'], 'perfect_csi': [True, False], 'detector': ['kbest', 'lmmse'], 'ebno_db': [-2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'speed': 3.0, 'batch_size_freq': 128, 'batch_size_time': 28, 'bler': [[0.865234375, 0.525390625, 0.236328125, 0.0703125, 0.022894965277777776, 0.0081787109375, 0.0031916920731707315, 0.0011800130208333333, 0.0007274208566108007, 0.000298828125, 0.000189453125, 0.000107421875, 4.6875e-05], [0.501953125, 0.2412109375, 0.13411458333333334, 0.06556919642857142, 0.037109375, 0.021073190789473683, 0.012251420454545454, 0.007244318181818182, 0.0038869121287128713, 0.0027646346830985913, 0.0015751008064516128, 0.0009838684538653367, 0.0007239105504587156], [0.994140625, 0.97265625, 0.849609375, 0.513671875, 0.234375, 0.115234375, 0.05126953125, 0.028878348214285716, 0.023092830882352942, 0.018694196428571428, 0.013671875, 0.012451171875, 0.013739224137931034], [0.919921875, 0.724609375, 0.533203125, 0.2802734375, 0.16536458333333334, 0.08984375, 0.056919642857142856, 0.043619791666666664, 0.035006009615384616, 0.02055921052631579, 0.017578125, 0.018465909090909092, 0.01532451923076923]], 'ber': [[0.08414149284362793, 0.03808903694152832, 0.013622879981994629, 0.00516200065612793, 0.0018940899107191297, 0.0006881306568781534, 0.0002859627328267912, 0.00012890001138051352, 9.374645169220823e-05, 3.3643484115600584e-05, 2.6883602142333983e-05, 1.2900114059448242e-05, 6.676435470581055e-06], [0.032366275787353516, 0.015960693359375, 0.009874105453491211, 0.004354306629725865, 0.00270201943137429, 0.0015692459909539473, 0.0008932893926447088, 0.0005442922765558416, 0.0002903820264457476, 0.00021878598441540356, 0.00013986518306116904, 7.878217911185171e-05, 6.43913898992976e-05], [0.15517663955688477, 0.12702298164367676, 0.08907222747802734, 0.036322832107543945, 0.015564680099487305, 0.008847415447235107, 0.005330264568328857, 0.003527675356183733, 0.0029088469112620633, 0.0025938579014369418, 0.002038750155218716, 0.0017822608351707458, 0.001927071604235419], [0.10343790054321289, 0.06611466407775879, 0.043680429458618164, 0.0217667818069458, 0.013199090957641602, 0.007306861877441406, 0.005208117621285575, 0.004094309277004666, 0.003994941711425781, 0.002383282310084293, 0.0023060985233472743, 0.002356225794011896, 0.002158962763272799]], 'duration': 4399.180883407593}\")\n",
    "print(\"Simulation duration: {:1.2f} [h]\".format(PUSCH_SIMS[\"duration\"]/3600))\n",
    "\n",
    "plt.figure()\n",
    "plt.title(\"5G NR PUSCH over UMi Channel Model (8x16)\")\n",
    "plt.xlabel(\"SNR (dB)\")\n",
    "plt.ylabel(\"BLER\")\n",
    "plt.grid(which=\"both\")\n",
    "plt.xlim([PUSCH_SIMS[\"ebno_db\"][0], PUSCH_SIMS[\"ebno_db\"][-1]])\n",
    "plt.ylim([1e-5, 1.0])\n",
    "\n",
    "i = 0\n",
    "legend = []\n",
    "for scenario in PUSCH_SIMS[\"scenario\"]:\n",
    "    for domain in PUSCH_SIMS[\"domain\"]:\n",
    "        for perfect_csi in PUSCH_SIMS[\"perfect_csi\"]:\n",
    "            for detector in PUSCH_SIMS[\"detector\"]:\n",
    "                plt.semilogy(PUSCH_SIMS[\"ebno_db\"], PUSCH_SIMS[\"bler\"][i])\n",
    "                i += 1\n",
    "                csi = \"Perf. CSI\" if perfect_csi else \"Imperf. CSI\"\n",
    "                det = \"K-Best\" if detector==\"kbest\" else \"LMMSE\"\n",
    "                legend.append(det + \" \" + csi)\n",
    "plt.legend(legend);"
   ]
  },
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   "source": [
    "Hopefully you have enjoyed this tutorial on Sionna's 5G NR PUSCH module!\n",
    "\n",
    "Please have a look at the [API documentation](https://nvlabs.github.io/sionna/api/sionna.html) of the various components or the other available [tutorials](https://nvlabs.github.io/sionna/tutorials.html) to learn more."
   ]
  },
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   "cell_type": "markdown",
   "id": "468507f5",
   "metadata": {},
   "source": []
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