{
"cells": [
{
"cell_type": "markdown",
"id": "c0222cc3-b667-4784-9b43-ca3129793c96",
"metadata": {},
"source": [
"# OFDM MIMO Channel Estimation and Detection"
]
},
{
"cell_type": "markdown",
"id": "220ef1ff-de24-451a-83ce-28cf0608de9d",
"metadata": {},
"source": [
"In this notebook, we will evaluate some of the OFDM channel estimation and MIMO\n",
"detection algorithms available in Sionna PHY.\n",
"\n",
"We will start by evaluating the mean square error (MSE) preformance of various channel estimation and interpolation methods.\n",
"\n",
"Then, we will compare some of the MIMO detection algorithms under both perfect and imperfect channel state information (CSI) in terms of uncoded symbol error rate (SER) and coded bit error rate (BER).\n",
"\n",
"The developed end-to-end models in this notebook are a great tool for benchmarking of MIMO receivers under realistic conditions. They can be easily extended to new channel estimation methods or MIMO detection algorithms."
]
},
{
"cell_type": "markdown",
"id": "d4a374f2-c8f3-45b7-9fe5-89123decb076",
"metadata": {},
"source": [
"For MSE evaluations, the block diagram of the system looks as follows:"
]
},
{
"cell_type": "markdown",
"id": "1151e856-875a-4c6a-8291-e1c00313eb25",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "7158bf5c-afb3-42f6-a5db-c928cb3b0422",
"metadata": {},
"source": [
"where the channel estimation module is highlighted as it is the focus of this evaluation. The channel covariance matrices are required for linear minimum mean square error (LMMSE) channel interpolation."
]
},
{
"cell_type": "markdown",
"id": "627b6480-7c04-42ad-98eb-2c4fc14d369e",
"metadata": {},
"source": [
"For uncoded SER evaluations, the block diagram of the system looks as follows:"
]
},
{
"cell_type": "markdown",
"id": "c9d2cbb9-499d-4615-b34c-13cad61228b1",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "53fc4e24-26e0-4812-a45f-b55f3b68042f",
"metadata": {},
"source": [
"where the channel estimation and detection modules are highlighted as they are the focus of this evaluation."
]
},
{
"cell_type": "markdown",
"id": "f622bbb9-9078-4881-8462-2eb69c1122d6",
"metadata": {},
"source": [
"Finally, for coded BER evaluations, the block diagram of the system looks as follows:"
]
},
{
"cell_type": "markdown",
"id": "4ff3e463-d95d-4816-9ae9-c0c185bf6b18",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "8123dc2f-f759-4615-a137-f10a1c5ef062",
"metadata": {},
"source": [
"## Table of Contents\n",
"* [Configuration and Imports](#Configuration-and-Imports)\n",
"* [Simulations parameters](#Simulation-parameters)\n",
"* [Estimation of the channel time, frequency, and spatial covariance matrices](#Estimation-of-the-channel-time,-frequency,-and-spatial-covariance-matrices)\n",
"* [Loading the channel covariance matrices](#Loading-the-channel-covariance-matrices)\n",
"* [Comparison of OFDM estimators](#Comparison-of-OFDM-estimators)\n",
"* [Comparison of MIMO detectors](#Comparison-of-MIMO-detectors)"
]
},
{
"cell_type": "markdown",
"id": "6fd7faab-7720-4039-9a16-65d53233dbb1",
"metadata": {},
"source": [
"## Configuration and Imports "
]
},
{
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"source": [
"# Import Sionna\n",
"try:\n",
" import sionna.phy\n",
"except ImportError as e:\n",
" import os\n",
" import sys\n",
" if 'google.colab' in sys.modules:\n",
" # Install Sionna in Google Colab\n",
" print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n",
" os.system(\"pip install sionna\")\n",
" os.kill(os.getpid(), 5)\n",
" else:\n",
" raise e\n",
"\n",
"sionna.phy.config.seed = 42 # Set seed for reproducible random number generation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9d3ff139",
"metadata": {
"execution": {
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"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import torch\n",
"\n",
"from sionna.phy import Block\n",
"from sionna.phy.mimo import StreamManagement\n",
"from sionna.phy.utils import sim_ber, ebnodb2no\n",
"from sionna.phy.mapping import Mapper, QAMSource, BinarySource\n",
"from sionna.phy.ofdm import ResourceGrid, ResourceGridMapper, LSChannelEstimator, \\\n",
" LMMSEInterpolator, LinearDetector, KBestDetector, \\\n",
" EPDetector, MMSEPICDetector\n",
"from sionna.phy.channel import GenerateOFDMChannel, OFDMChannel, gen_single_sector_topology\n",
"from sionna.phy.channel.tr38901 import UMi, Antenna, PanelArray\n",
"from sionna.phy.fec.ldpc import LDPC5GEncoder, LDPC5GDecoder"
]
},
{
"cell_type": "markdown",
"id": "81ead318-3f42-48b0-b140-62dcbbb7baff",
"metadata": {},
"source": [
"## Simulation parameters"
]
},
{
"cell_type": "markdown",
"id": "575c8802-bc4c-4db7-bb96-67c897735d00",
"metadata": {},
"source": [
"The next cell defines the simulation parameters used throughout this notebook.\n",
"\n",
"This includes the OFDM waveform parameters, [antennas geometries and patterns](https://nvlabs.github.io/sionna/phy/api/channel/wireless/sionna.phy.channel.tr38901.PanelArray.html), and the [3GPP UMi channel model](https://nvlabs.github.io/sionna/phy/api/channel/wireless/sionna.phy.channel.tr38901.UMi.html)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c770ffeb-5396-4c2f-af31-4b4d82a805ae",
"metadata": {
"execution": {
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"source": [
"NUM_OFDM_SYMBOLS = 14\n",
"FFT_SIZE = 12*4 # 4 PRBs\n",
"SUBCARRIER_SPACING = 30e3 # Hz\n",
"CARRIER_FREQUENCY = 3.5e9 # Hz\n",
"SPEED = 3. # m/s\n",
"\n",
"# The user terminals (UTs) are equipped with a single antenna\n",
"# with vertial polarization.\n",
"UT_ANTENNA = Antenna(polarization='single',\n",
" polarization_type='V',\n",
" antenna_pattern='omni', # Omnidirectional antenna pattern\n",
" carrier_frequency=CARRIER_FREQUENCY)\n",
"\n",
"# The base station is equipped with an antenna\n",
"# array of 8 cross-polarized antennas,\n",
"# resulting in a total of 16 antenna elements.\n",
"NUM_RX_ANT = 16\n",
"BS_ARRAY = PanelArray(num_rows_per_panel=4,\n",
" num_cols_per_panel=2,\n",
" polarization='dual',\n",
" polarization_type='cross',\n",
" antenna_pattern='38.901', # 3GPP 38.901 antenna pattern\n",
" carrier_frequency=CARRIER_FREQUENCY)\n",
"\n",
"# 3GPP UMi channel model is considered\n",
"CHANNEL_MODEL = UMi(carrier_frequency=CARRIER_FREQUENCY,\n",
" o2i_model='low',\n",
" ut_array=UT_ANTENNA,\n",
" bs_array=BS_ARRAY,\n",
" direction='uplink',\n",
" enable_shadow_fading=False,\n",
" enable_pathloss=False)"
]
},
{
"cell_type": "markdown",
"id": "a07a86d6-73e8-457d-b44c-98753023ca52",
"metadata": {},
"source": [
"## Estimation of the channel time, frequency, and spatial covariance matrices"
]
},
{
"cell_type": "markdown",
"id": "4682a1c0-4c2f-41b0-b244-d178d06c1264",
"metadata": {},
"source": [
"The linear minimum mean square (LMMSE) interpolation method requires knowledge of the time (i.e., across OFDM symbols), frequency (i.e., across sub-carriers), and spatial (i.e., across receive antennas) covariance matrices of the channel frequency response.\n",
"\n",
"These are estimated in this section using Monte Carlo sampling.\n",
"\n",
"We explain below how this is achieved for the frequency covariance matrix. The same approach is used for the time and spatial covariance matrices.\n",
"\n",
"Let $N$ be the number of sub-carriers.\n",
"The first step for estimating the frequency covariance matrix is to sample the channel model in order to build a set of frequency-domain channel realizations $\\left\\{ \\mathbf{h}_k \\right\\}, 1 \\leq k \\leq K$, where $K$ is the number of samples and $\\mathbf{h}_k \\in \\mathbb{C}^{N}$ are complex-valued samples of the channel frequency response.\n",
"\n",
"The frequency covariance matrix $\\mathbf{R}^{(f)} \\in \\mathbb{C}^{N \\times N}$ is then estimated by\n",
"\n",
"\\begin{equation}\n",
"\\mathbf{R}^{(f)} \\approx \\frac{1}{K} \\sum_{k = 1}^K \\mathbf{h}_k \\mathbf{h}_k^{\\mathrm{H}}\n",
"\\end{equation}\n",
"\n",
"where we assume that the frequency-domain channel response has zero mean.\n",
"\n",
"The following cells implement this process for all three dimensions (frequency, time, and space).\n"
]
},
{
"cell_type": "markdown",
"id": "0c6fdb83-e5b9-4521-a15a-59bfe337f041",
"metadata": {},
"source": [
"The next cell defines a [resource grid](https://nvlabs.github.io/sionna/phy/api/ofdm/resource_grid.html) and an [OFDM channel generator](https://nvlabs.github.io/sionna/phy/api/channel/wireless/sionna.phy.channel.GenerateOFDMChannel.html) for sampling the channel in the frequency domain."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "0ae51854-9597-409d-8ecf-a1d85e02b3cd",
"metadata": {
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"source": [
"rg = ResourceGrid(num_ofdm_symbols=NUM_OFDM_SYMBOLS,\n",
" fft_size=FFT_SIZE,\n",
" subcarrier_spacing=SUBCARRIER_SPACING)\n",
"channel_sampler = GenerateOFDMChannel(CHANNEL_MODEL, rg)"
]
},
{
"cell_type": "markdown",
"id": "65802f06-150a-4e91-a68b-05fa44aa3eaf",
"metadata": {},
"source": [
"Then, a function that samples the channel is defined.\n",
"It randomly samples a network topology for every batch and for every batch example using the [appropriate utility function](https://nvlabs.github.io/sionna/phy/api/channel/wireless/sionna.phy.channel.gen_single_sector_topology.html)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5a71816c-bd1e-4789-9ea1-db6f5cf1525c",
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"source": [
"def sample_channel(batch_size):\n",
" # Sample random topologies\n",
" topology = gen_single_sector_topology(batch_size, 1, 'umi', min_ut_velocity=SPEED, max_ut_velocity=SPEED)\n",
" CHANNEL_MODEL.set_topology(*topology)\n",
"\n",
" # Sample channel frequency responses\n",
" # [batch size, 1, num_rx_ant, 1, 1, num_ofdm_symbols, fft_size]\n",
" h_freq = channel_sampler(batch_size)\n",
" # [batch size, num_rx_ant, num_ofdm_symbols, fft_size]\n",
" h_freq = h_freq[:,0,:,0,0]\n",
"\n",
" return h_freq"
]
},
{
"cell_type": "markdown",
"id": "2bd07c16-595e-4880-b2aa-e162e75f9d55",
"metadata": {},
"source": [
"We now define a function that estimates the frequency, time, and spatial covariance matrcies using Monte Carlo sampling."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1cc6d3e1-9abb-45f4-ad66-27fa3ff5654a",
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"source": [
"def estimate_covariance_matrices(num_it, batch_size):\n",
" freq_cov_mat = torch.zeros([FFT_SIZE, FFT_SIZE], dtype=torch.complex64, device=sionna.phy.config.device)\n",
" time_cov_mat = torch.zeros([NUM_OFDM_SYMBOLS, NUM_OFDM_SYMBOLS], dtype=torch.complex64, device=sionna.phy.config.device)\n",
" space_cov_mat = torch.zeros([NUM_RX_ANT, NUM_RX_ANT], dtype=torch.complex64, device=sionna.phy.config.device)\n",
" for _ in range(num_it):\n",
" # [batch size, num_rx_ant, num_ofdm_symbols, fft_size]\n",
" h_samples = sample_channel(batch_size)\n",
"\n",
" #################################\n",
" # Estimate frequency covariance\n",
" #################################\n",
" # [batch size, num_rx_ant, fft_size, num_ofdm_symbols]\n",
" h_samples_ = h_samples.permute(0, 1, 3, 2)\n",
" # [batch size, num_rx_ant, fft_size, fft_size]\n",
" freq_cov_mat_ = h_samples_ @ h_samples_.mH\n",
" # [fft_size, fft_size]\n",
" freq_cov_mat_ = freq_cov_mat_.mean(dim=(0, 1))\n",
" # [fft_size, fft_size]\n",
" freq_cov_mat += freq_cov_mat_\n",
"\n",
" ################################\n",
" # Estimate time covariance\n",
" ################################\n",
" # [batch size, num_rx_ant, num_ofdm_symbols, fft_size]\n",
" time_cov_mat_ = h_samples @ h_samples.mH\n",
" # [num_ofdm_symbols, num_ofdm_symbols]\n",
" time_cov_mat_ = time_cov_mat_.mean(dim=(0, 1))\n",
" # [num_ofdm_symbols, num_ofdm_symbols]\n",
" time_cov_mat += time_cov_mat_\n",
"\n",
" ###############################\n",
" #Â Estimate spatial covariance\n",
" ###############################\n",
" # [batch size, num_ofdm_symbols, num_rx_ant, fft_size]\n",
" h_samples_ = h_samples.permute(0, 2, 1, 3)\n",
" # [batch size, num_ofdm_symbols, num_rx_ant, num_rx_ant]\n",
" space_cov_mat_ = h_samples_ @ h_samples_.mH\n",
" # [num_rx_ant, num_rx_ant]\n",
" space_cov_mat_ = space_cov_mat_.mean(dim=(0,1))\n",
" # [num_rx_ant, num_rx_ant]\n",
" space_cov_mat += space_cov_mat_\n",
"\n",
" freq_cov_mat /= torch.complex(torch.tensor(float(NUM_OFDM_SYMBOLS*num_it)), torch.tensor(0.0))\n",
" time_cov_mat /= torch.complex(torch.tensor(float(FFT_SIZE*num_it)), torch.tensor(0.0))\n",
" space_cov_mat /= torch.complex(torch.tensor(float(FFT_SIZE*num_it)), torch.tensor(0.0))\n",
"\n",
" return freq_cov_mat, time_cov_mat, space_cov_mat"
]
},
{
"cell_type": "markdown",
"id": "d5e444cb-58d7-42c7-9e49-cdb6f9eb4aaa",
"metadata": {},
"source": [
"We then compute the estimates by executing the function defined in the previous cell.\n",
"\n",
"The batch size and number of iterations determine the total number of samples, i.e.,\n",
"\n",
"```\n",
"number of samples = batch_size x num_iterations\n",
"```\n",
"\n",
"and hence control the tradeoff between the accuracy of the estimates and the time needed for their computation."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8ea94466-bb28-4ed5-a7cb-6e1cf82f86c0",
"metadata": {
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"source": [
"batch_size = 1000\n",
"num_iterations = 100\n",
"FREQ_COV_MAT, TIME_COV_MAT, SPACE_COV_MAT = estimate_covariance_matrices(batch_size, num_iterations)\n",
"FREQ_COV_MAT = FREQ_COV_MAT.cpu().numpy()\n",
"TIME_COV_MAT = TIME_COV_MAT.cpu().numpy()\n",
"SPACE_COV_MAT = SPACE_COV_MAT.cpu().numpy()"
]
},
{
"cell_type": "markdown",
"id": "0ddaf455-cae8-4248-8979-a9428c4298d6",
"metadata": {},
"source": [
"Finally, the estimated matrices are saved (as numpy arrays) for future use."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6048932c-486a-419f-81c2-62b1bc6c9ff5",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# FREQ_COV_MAT : [fft_size, fft_size]\n",
"# TIME_COV_MAT : [num_ofdm_symbols, num_ofdm_symbols]\n",
"# SPACE_COV_MAT : [num_rx_ant, num_rx_ant]\n",
"\n",
"np.save('freq_cov_mat', FREQ_COV_MAT)\n",
"np.save('time_cov_mat', TIME_COV_MAT)\n",
"np.save('space_cov_mat', SPACE_COV_MAT)"
]
},
{
"cell_type": "markdown",
"id": "2770ca3d-19aa-4337-ab2b-8b184f2a0ddd",
"metadata": {},
"source": [
"## Loading the channel covariance matrices"
]
},
{
"cell_type": "markdown",
"id": "d4bab810-fc78-4a0c-bac4-f1da9f2bc07d",
"metadata": {},
"source": [
"The next cell loads saved estimates of the time, frequency, and space covariance matrices."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "b20bce1a-9a6c-4842-aaad-3b659931cb6e",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"FREQ_COV_MAT = np.load('freq_cov_mat.npy')\n",
"TIME_COV_MAT = np.load('time_cov_mat.npy')\n",
"SPACE_COV_MAT = np.load('space_cov_mat.npy')"
]
},
{
"cell_type": "markdown",
"id": "c1ee48c1-6817-4c40-9b7f-447c5a34c9b8",
"metadata": {},
"source": [
"We then visualize the loaded matrices.\n",
"\n",
"As one can see, the frequency correlation slowly decays with increasing spectral distance.\n",
"\n",
"The time-correlation is much stronger as the mobility low. The covariance matrix is hence very badly conditioned with rank almost equal to one.\n",
"\n",
"The spatial covariance matrix has a regular structure which is determined by the array geometry and polarization of its elements."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7e316b0f-bbfd-4c75-9204-9a0cd49aa63b",
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{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(3,2, figsize=(10,12))\n",
"fig.suptitle(\"Time and frequency channel covariance matrices\")\n",
"\n",
"ax[0,0].set_title(\"Freq. cov. Real\")\n",
"im = ax[0,0].imshow(FREQ_COV_MAT.real, vmin=-0.3, vmax=1.8)\n",
"ax[0,1].set_title(\"Freq. cov. Imag\")\n",
"im = ax[0,1].imshow(FREQ_COV_MAT.imag, vmin=-0.3, vmax=1.8)\n",
"\n",
"ax[1,0].set_title(\"Time cov. Real\")\n",
"im = ax[1,0].imshow(TIME_COV_MAT.real, vmin=-0.3, vmax=1.8)\n",
"ax[1,1].set_title(\"Time cov. Imag\")\n",
"im = ax[1,1].imshow(TIME_COV_MAT.imag, vmin=-0.3, vmax=1.8)\n",
"\n",
"ax[2,0].set_title(\"Space cov. Real\")\n",
"im = ax[2,0].imshow(SPACE_COV_MAT.real, vmin=-0.3, vmax=1.8)\n",
"ax[2,1].set_title(\"Space cov. Imag\")\n",
"im = ax[2,1].imshow(SPACE_COV_MAT.imag, vmin=-0.3, vmax=1.8)\n",
"\n",
"fig.subplots_adjust(right=0.8)\n",
"cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n",
"fig.colorbar(im, cax=cbar_ax);"
]
},
{
"cell_type": "markdown",
"id": "b5867869-195c-4daf-9b25-f226ea34aca2",
"metadata": {},
"source": [
"## Comparison of OFDM estimators"
]
},
{
"cell_type": "markdown",
"id": "0dabc5a8-37a4-4609-9966-ca3374602cf7",
"metadata": {},
"source": [
"This section focuses on comparing the available OFDM channel estimators in Sionna for the considered setup."
]
},
{
"cell_type": "markdown",
"id": "1bf539fc-04fd-48be-99a4-7afc25ae0b2f",
"metadata": {},
"source": [
"OFDM channel estimation consists of two steps:\n",
"\n",
"1. Channel estimation at pilot-carrying resource elements using [least-squares (LS)](https://nvlabs.github.io/sionna/phy/api/ofdm/sionna.phy.ofdm.LSChannelEstimator.html).\n",
"\n",
"2. Interpolation for data-carrying resource elements, for which three methods are available in Sionna:\n",
"\n",
"- [Nearest-neighbor](https://nvlabs.github.io/sionna/phy/api/ofdm/sionna.phy.ofdm.NearestNeighborInterpolator.html), which uses the channel estimate of the nearest pilot\n",
"- [Linear](https://nvlabs.github.io/sionna/phy/api/ofdm/sionna.phy.ofdm.LinearInterpolator.html), with optional averaging over the OFDM symbols (time dimension) for low mobility scenarios\n",
"- [LMMSE](https://nvlabs.github.io/sionna/phy/api/ofdm/sionna.phy.ofdm.LMMSEInterpolator.html), which requires knowledge of the time and frequency covariance matrices\n",
"\n",
"The LMMSE interpolator also features optional spatial smoothin, which requires the spatial covarance matrix. The [API documentation](https://nvlabs.github.io/sionna/phy/api/ofdm/sionna.phy.ofdm.LMMSEInterpolator.html) explains in more detail how this interpolator operates."
]
},
{
"cell_type": "markdown",
"id": "93b9fb03-97cd-4d9b-adb6-e0b00be506fb",
"metadata": {},
"source": [
"### End-to-end model"
]
},
{
"cell_type": "markdown",
"id": "b1568b07-702f-40a8-afb8-66115a5c4ea0",
"metadata": {},
"source": [
"In the next cell, we will create a Sionna Block which uses the interpolation method specified at initialization.\n",
"\n",
"It computes the mean square error (MSE) for a specified batch size and signal-to-noise ratio (SNR) (in dB).\n",
"\n",
"The following interpolation methods are available (set through the `int_method` parameter):\n",
"\n",
"- `\"nn\"` : Nearest-neighbor interpolation\n",
"- `\"lin\"` : Linear interpolation\n",
"- `\"lmmse\"` : LMMSE interpolation\n",
"\n",
"When LMMSE interpolation is used, it is required to specified the order in which interpolation and optional spatial smoothing is performed.\n",
"This is achieved using the `lmmse_order` parameter. For example, setting this parameter to `\"f-t\"` leads to frequency interpolation being performed first followed by time interpolation, and no spatial smoothing.\n",
"Setting it to `\"t-f-s\"` leads to time interpolation being performed first, followed by frequency interpolation, and finally spatial smoothing. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "e83d1d4a-7153-4856-b1ab-79177ad846a1",
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-14T01:12:18.086392Z",
"iopub.status.busy": "2026-02-14T01:12:18.086251Z",
"iopub.status.idle": "2026-02-14T01:12:18.092620Z",
"shell.execute_reply": "2026-02-14T01:12:18.091826Z"
}
},
"outputs": [],
"source": [
"class MIMOOFDMLink(Block):\n",
"\n",
" def __init__(self, int_method, lmmse_order=None, **kwargs):\n",
" super().__init__(kwargs)\n",
"\n",
" assert int_method in ('nn', 'lin', 'lmmse')\n",
"\n",
"\n",
" # Configure the resource grid\n",
" rg = ResourceGrid(num_ofdm_symbols=NUM_OFDM_SYMBOLS,\n",
" fft_size=FFT_SIZE,\n",
" subcarrier_spacing=SUBCARRIER_SPACING,\n",
" num_tx=1,\n",
" pilot_pattern=\"kronecker\",\n",
" pilot_ofdm_symbol_indices=[2,11])\n",
" self.rg = rg\n",
"\n",
" # Stream management\n",
" # Only a sinlge UT is considered for channel estimation\n",
" sm = StreamManagement([[1]], 1)\n",
"\n",
" ##################################\n",
" # Transmitter\n",
" ##################################\n",
"\n",
" self.qam_source = QAMSource(num_bits_per_symbol=2) # Modulation order does not impact the channel estimation. Set to QPSK\n",
" self.rg_mapper = ResourceGridMapper(rg)\n",
"\n",
" ##################################\n",
" # Channel\n",
" ##################################\n",
" self.channel_model = UMi(carrier_frequency=CARRIER_FREQUENCY,\n",
" o2i_model='low',\n",
" ut_array=UT_ANTENNA,\n",
" bs_array=BS_ARRAY,\n",
" direction='uplink',\n",
" enable_shadow_fading=False,\n",
" enable_pathloss=False)\n",
"\n",
" self.channel = OFDMChannel(self.channel_model, rg, return_channel=True)\n",
"\n",
" ###################################\n",
" # Receiver\n",
" ###################################\n",
"\n",
" # Channel estimation\n",
" freq_cov_mat = torch.tensor(FREQ_COV_MAT, dtype=torch.complex64, device=sionna.phy.config.device)\n",
" time_cov_mat = torch.tensor(TIME_COV_MAT, dtype=torch.complex64, device=sionna.phy.config.device)\n",
" space_cov_mat = torch.tensor(SPACE_COV_MAT, dtype=torch.complex64, device=sionna.phy.config.device)\n",
" if int_method == 'nn':\n",
" self.channel_estimator = LSChannelEstimator(rg, interpolation_type='nn')\n",
" elif int_method == 'lin':\n",
" self.channel_estimator = LSChannelEstimator(rg, interpolation_type='lin')\n",
" elif int_method == 'lmmse':\n",
" lmmse_int_freq_first = LMMSEInterpolator(rg.pilot_pattern, time_cov_mat, freq_cov_mat, space_cov_mat, order=lmmse_order)\n",
" self.channel_estimator = LSChannelEstimator(rg, interpolator=lmmse_int_freq_first)\n",
"\n",
" def call(self, batch_size, snr_db):\n",
"\n",
"\n",
" ##################################\n",
" # Transmitter\n",
" ##################################\n",
"\n",
" x = self.qam_source([batch_size, 1, 1, self.rg.num_data_symbols])\n",
" x_rg = self.rg_mapper(x)\n",
"\n",
" ##################################\n",
" # Channel\n",
" ##################################\n",
"\n",
" no = torch.pow(torch.tensor(10.0, device=sionna.phy.config.device), -snr_db / 10.0)\n",
" topology = gen_single_sector_topology(batch_size, 1, 'umi', min_ut_velocity=SPEED, max_ut_velocity=SPEED)\n",
" self.channel_model.set_topology(*topology)\n",
" y_rg, h_freq = self.channel(x_rg, no)\n",
"\n",
" ###################################\n",
" # Channel estimation\n",
" ###################################\n",
"\n",
" h_hat,_ = self.channel_estimator(y_rg,no)\n",
"\n",
" ###################################\n",
" # MSE\n",
" ###################################\n",
"\n",
" mse = torch.mean((h_freq - h_hat).abs().square())\n",
"\n",
" return mse"
]
},
{
"cell_type": "markdown",
"id": "e20e3285",
"metadata": {},
"source": [
"The next cell defines a function for evaluating the mean square error (MSE) of a `model` over a range of SNRs (`snr_dbs`).\n",
"\n",
"The `batch_size` and `num_it` parameters control the number of samples used to compute the MSE for each SNR value."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "2e89a42b-ca66-4448-b6da-ccf98d0ca3e1",
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-14T01:12:18.094753Z",
"iopub.status.busy": "2026-02-14T01:12:18.094621Z",
"iopub.status.idle": "2026-02-14T01:12:18.097556Z",
"shell.execute_reply": "2026-02-14T01:12:18.096812Z"
}
},
"outputs": [],
"source": [
"def evaluate_mse(model, snr_dbs, batch_size, num_it):\n",
"\n",
" mses = []\n",
" for snr_db in snr_dbs:\n",
"\n",
" mse_ = 0.0\n",
" for _ in range(num_it):\n",
" mse_ += model(batch_size, snr_db).cpu().numpy()\n",
" # Averaging over the number of iterations\n",
" mse_ /= float(num_it)\n",
" mses.append(mse_)\n",
"\n",
" return mses"
]
},
{
"cell_type": "markdown",
"id": "dc14c206",
"metadata": {},
"source": [
"The next cell defines the evaluation parameters."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d8de113a-07ba-40e3-83ce-03c2fe0fd640",
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-14T01:12:18.099725Z",
"iopub.status.busy": "2026-02-14T01:12:18.099602Z",
"iopub.status.idle": "2026-02-14T01:12:18.102699Z",
"shell.execute_reply": "2026-02-14T01:12:18.101866Z"
}
},
"outputs": [],
"source": [
"# Range of SNR (in dB)\n",
"SNR_DBs = np.linspace(-10.0, 20.0, 20)\n",
"\n",
"# Number of iterations and batch size.\n",
"# These parameters control the number of samples used to compute each SNR value.\n",
"# The higher the number of samples is, the more accurate the MSE estimation is, at\n",
"# the cost of longer compute time.\n",
"BATCH_SIZE = 512\n",
"NUM_IT = 10\n",
"\n",
"# Interpolation/filtering order for the LMMSE interpolator.\n",
"# All valid configurations are listed.\n",
"# Some are commented to speed-up simulations.\n",
"# Uncomment configurations to evaluate them!\n",
"ORDERS = ['s-t-f', # Space - time - frequency\n",
" #'s-f-t', # Space - frequency - time\n",
" #'t-s-f', # Time - space - frequency\n",
" 't-f-s', # Time - frequency - space\n",
" #'f-t-s', # Frequency - time - space\n",
" #'f-s-t', # Frequency - space- time\n",
" #'f-t', # Frequency - time (no spatial smoothing)\n",
" 't-f' # Time - frequency (no spatial smoothing)\n",
"]"
]
},
{
"cell_type": "markdown",
"id": "cb63359c",
"metadata": {},
"source": [
"The next cell evaluates the nearest-neighbor, linear, and LMMSE interpolator.\n",
"For the LMMSE interpolator, we loop through the configuration listed in\n",
"`ORDERS`. This can take a while."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "28b4a2dd-eb4e-449f-958c-9d3985296d03",
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-14T01:12:18.104603Z",
"iopub.status.busy": "2026-02-14T01:12:18.104482Z",
"iopub.status.idle": "2026-02-14T01:15:43.185799Z",
"shell.execute_reply": "2026-02-14T01:15:43.184632Z"
}
},
"outputs": [],
"source": [
"MSES = {}\n",
"\n",
"# Nearest-neighbor interpolation\n",
"e2e = MIMOOFDMLink(\"nn\")\n",
"e2e.channel_model.allocate_topology_tensors(batch_size=BATCH_SIZE, num_bs=1, num_ut=1)\n",
"MSES['nn'] = evaluate_mse(e2e, SNR_DBs, BATCH_SIZE, NUM_IT)\n",
"\n",
"# Linear interpolation\n",
"e2e = MIMOOFDMLink(\"lin\")\n",
"e2e.channel_model.allocate_topology_tensors(batch_size=BATCH_SIZE, num_bs=1, num_ut=1)\n",
"MSES['lin'] = evaluate_mse(e2e, SNR_DBs, BATCH_SIZE, NUM_IT)\n",
"\n",
"# LMMSE\n",
"for order in ORDERS:\n",
" e2e = MIMOOFDMLink(\"lmmse\", order)\n",
" e2e.channel_model.allocate_topology_tensors(batch_size=BATCH_SIZE, num_bs=1, num_ut=1)\n",
" MSES[f\"lmmse: {order}\"] = evaluate_mse(e2e, SNR_DBs, BATCH_SIZE, NUM_IT)\n"
]
},
{
"cell_type": "markdown",
"id": "cf1d55b0",
"metadata": {},
"source": [
"Finally, we plot the MSE."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "8a83a603-88ed-41d6-9eb6-927f73efe384",
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-14T01:15:43.188116Z",
"iopub.status.busy": "2026-02-14T01:15:43.187979Z",
"iopub.status.idle": "2026-02-14T01:15:43.391723Z",
"shell.execute_reply": "2026-02-14T01:15:43.390811Z"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,6))\n",
"\n",
"for est_label in MSES:\n",
" plt.semilogy(SNR_DBs, MSES[est_label], label=est_label)\n",
"\n",
"plt.xlabel(r\"SNR (dB)\")\n",
"plt.ylabel(\"MSE\")\n",
"plt.legend()\n",
"plt.grid(True)"
]
},
{
"cell_type": "markdown",
"id": "e4e29531-1b22-4c01-a46a-91bc8834ea96",
"metadata": {},
"source": [
"Unsurprisingly, the LMMSE interpolator leads to more accurate estimates compared to the two other methods, as it leverages knowledge of the the channel statistics.\n",
"Moreover, the order in which the LMMSE interpolation steps are performed strongly impacts the accuracy of the estimator. This is because the LMMSE interpolation operates in one dimension at a time which is not equivalent to full-blown LMMSE estimation across all dimensions at one.\n",
"\n",
"Also note that the order that leads to the best accuracy depends on the channel statistics. As a rule of thumb, it might be good to start with the dimension that is most strongly correlated (i.e., time in our example)."
]
},
{
"cell_type": "markdown",
"id": "b4556af5-ecaa-4770-86ae-165238ef0e21",
"metadata": {},
"source": [
"## Comparison of MIMO detectors"
]
},
{
"cell_type": "markdown",
"id": "07870ad0-b59f-4227-ab15-fba5331a89a8",
"metadata": {},
"source": [
"An OFDM MIMO receiver consists of two stages: **OFDM channel estimation** and **MIMO detection**.\n",
"\n",
"While the previous section focused on OFDM channel estimation, this section focuses now on MIMO detection.\n",
"\n",
"The following MIMO detection algorithms, all available out-of-the-box in Sionna, are considered:\n",
"\n",
"- [LMMSE equalization followed by APP demapping](https://nvlabs.github.io/sionna/phy/api/mimo/sionna.phy.mimo.LinearDetector.html)\n",
"- [K-Best detection](https://nvlabs.github.io/sionna/phy/api/mimo/sionna.phy.mimo.KBestDetector.html)\n",
"- [EP detection](https://nvlabs.github.io/sionna/phy/api/mimo/sionna.phy.mimo.EPDetector.html)\n",
"- [MMSE-PIC detection](https://nvlabs.github.io/sionna/phy/api/mimo/sionna.phy.mimo.MMSEPICDetector.html)\n",
"\n",
"Both perfect and imperfect channel state information is considered in the simulations.\n",
"LS estimation combined with LMMSE interpolation is used, with time-frequency-space smoothing (in this order, i.e., `order='t-f-s'`)."
]
},
{
"cell_type": "markdown",
"id": "5cda3dc1-1317-4b46-bcb0-8b3ef8a02e97",
"metadata": {},
"source": [
"### End-to-end model"
]
},
{
"cell_type": "markdown",
"id": "1bad6adf-eff6-451a-bef4-fe24731b1769",
"metadata": {},
"source": [
"An end-to-end model is created in the next cell as a Sionna Block, which uses the detection method specified at initialization.\n",
"\n",
"It computes either the coded bit error rate (BER) or the uncoded symbol error rate (SER), for a specified batch size, $E_b/N_0$ (in dB), and QAM modulation with a specified modulation order.\n",
"When computing the BER, a 5G LDPC code is used with the specified coderate.\n",
"\n",
"The following MIMO detection methods are considered (set through the `det_param` parameter):\n",
"\n",
"- `\"lmmse\"` : No parameter needed\n",
"- `\"k-best\"` : List size `k`, defaults to 64\n",
"- `\"ep\"` : Number of iterations `l`, defaults to 10\n",
"- `\"mmse-pic\"` : Number of self-iterations `num_it`, defaults to 4\n",
"\n",
"The `det_param` parameter corresponds to either `k`, `l`, or `num_it`, for K-Best, EP, or MMSE-PIC, respectively. If set to `None`, a default value is used according to the selected detector.\n",
"\n",
"The `perf_csi` parameter controls whether perfect CSI is assumed or not. If set to `False`, then LS combined with LMMSE interpolation is used to estimate the channel.\n",
"\n",
"You can easily add your own MIMO detector and channel estimator to this model for a fair and realistic benchmark."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "de827343-04d6-4a52-8ffe-f26c76bd8a6d",
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-14T01:15:43.394456Z",
"iopub.status.busy": "2026-02-14T01:15:43.394313Z",
"iopub.status.idle": "2026-02-14T01:15:43.404707Z",
"shell.execute_reply": "2026-02-14T01:15:43.403862Z"
}
},
"outputs": [],
"source": [
"class MIMOOFDMLink(Block):\n",
"\n",
" def __init__(self, output, det_method, perf_csi, num_tx, num_bits_per_symbol, det_param=None, coderate=0.5, **kwargs):\n",
" super().__init__(kwargs)\n",
"\n",
" assert det_method in ('lmmse', 'k-best', 'ep', 'mmse-pic'), \"Unknown detection method\"\n",
"\n",
" self._output = output\n",
" self.num_tx = num_tx\n",
" self.num_bits_per_symbol = num_bits_per_symbol\n",
" self.coderate = coderate\n",
" self.det_method = det_method\n",
" self.perf_csi = perf_csi\n",
"\n",
" # Configure the resource grid\n",
" rg = ResourceGrid(num_ofdm_symbols=NUM_OFDM_SYMBOLS,\n",
" fft_size=FFT_SIZE,\n",
" subcarrier_spacing=SUBCARRIER_SPACING,\n",
" num_tx=num_tx,\n",
" pilot_pattern=\"kronecker\",\n",
" pilot_ofdm_symbol_indices=[2,11])\n",
" self.rg = rg\n",
"\n",
" # Stream management\n",
" sm = StreamManagement(np.ones([1,num_tx], int), 1)\n",
"\n",
" # Codeword length and number of information bits per codeword\n",
" n = int(rg.num_data_symbols*num_bits_per_symbol)\n",
" k = int(coderate*n)\n",
" self.n = n\n",
" self.k = k\n",
"\n",
" # If output is symbol, then no FEC is used and hard decision are output\n",
" hard_out = (output == \"symbol\")\n",
" coded = (output == \"bit\")\n",
" self.hard_out = hard_out\n",
" self.coded = coded\n",
"\n",
" ##################################\n",
" # Transmitter\n",
" ##################################\n",
"\n",
" self.binary_source = BinarySource()\n",
" self.mapper = Mapper(constellation_type=\"qam\", num_bits_per_symbol=num_bits_per_symbol, return_indices=True)\n",
" self.rg_mapper = ResourceGridMapper(rg)\n",
" if coded:\n",
" self.encoder = LDPC5GEncoder(k, n, num_bits_per_symbol=num_bits_per_symbol)\n",
"\n",
" ##################################\n",
" # Channel\n",
" ##################################\n",
" self.channel_model = UMi(carrier_frequency=CARRIER_FREQUENCY,\n",
" o2i_model='low',\n",
" ut_array=UT_ANTENNA,\n",
" bs_array=BS_ARRAY,\n",
" direction='uplink',\n",
" enable_shadow_fading=False,\n",
" enable_pathloss=False)\n",
"\n",
" self.channel = OFDMChannel(self.channel_model, rg, return_channel=True)\n",
"\n",
" ###################################\n",
" # Receiver\n",
" ###################################\n",
"\n",
" # Channel estimation\n",
" if not self.perf_csi:\n",
" freq_cov_mat = torch.tensor(FREQ_COV_MAT, dtype=torch.complex64, device=sionna.phy.config.device)\n",
" time_cov_mat = torch.tensor(TIME_COV_MAT, dtype=torch.complex64, device=sionna.phy.config.device)\n",
" space_cov_mat = torch.tensor(SPACE_COV_MAT, dtype=torch.complex64, device=sionna.phy.config.device)\n",
" lmmse_int_time_first = LMMSEInterpolator(rg.pilot_pattern, time_cov_mat, freq_cov_mat, space_cov_mat, order='t-f-s')\n",
" self.channel_estimator = LSChannelEstimator(rg, interpolator=lmmse_int_time_first)\n",
"\n",
" # Detection\n",
" if det_method == \"lmmse\":\n",
" self.detector = LinearDetector(\"lmmse\", output, \"app\", rg, sm, constellation_type=\"qam\", num_bits_per_symbol=num_bits_per_symbol, hard_out=hard_out)\n",
" elif det_method == 'k-best':\n",
" if det_param is None:\n",
" k = 64\n",
" else:\n",
" k = det_param\n",
" self.detector = KBestDetector(output, num_tx, k, rg, sm, constellation_type=\"qam\", num_bits_per_symbol=num_bits_per_symbol, hard_out=hard_out)\n",
" elif det_method == \"ep\":\n",
" if det_param is None:\n",
" l = 10\n",
" else:\n",
" l = det_param\n",
" self.detector = EPDetector(output, rg, sm, num_bits_per_symbol, l=l, hard_out=hard_out)\n",
" elif det_method == 'mmse-pic':\n",
" if det_param is None:\n",
" l = 4\n",
" else:\n",
" l = det_param\n",
" self.detector = MMSEPICDetector(output, 'app', rg, sm, num_iter=l, constellation_type=\"qam\", num_bits_per_symbol=num_bits_per_symbol, hard_out=hard_out)\n",
"\n",
" if coded:\n",
" self.decoder = LDPC5GDecoder(self.encoder, hard_out=False)\n",
"\n",
" def call(self, batch_size, ebno_db):\n",
"\n",
"\n",
" ##################################\n",
" # Transmitter\n",
" ##################################\n",
"\n",
" if self.coded:\n",
" b = self.binary_source([batch_size, self.num_tx, 1, self.k])\n",
" c = self.encoder(b)\n",
" else:\n",
" c = self.binary_source([batch_size, self.num_tx, 1, self.n])\n",
" bits_shape = c.shape\n",
" x,x_ind = self.mapper(c)\n",
" x_rg = self.rg_mapper(x)\n",
"\n",
" ##################################\n",
" # Channel\n",
" ##################################\n",
"\n",
" no = ebnodb2no(ebno_db, self.num_bits_per_symbol, self.coderate, resource_grid=self.rg)\n",
" topology = gen_single_sector_topology(batch_size, self.num_tx, 'umi', min_ut_velocity=SPEED, max_ut_velocity=SPEED)\n",
" self.channel_model.set_topology(*topology)\n",
" y_rg, h_freq = self.channel(x_rg, no)\n",
"\n",
" ###################################\n",
" # Receiver\n",
" ###################################\n",
"\n",
" # Channel estimation\n",
" if self.perf_csi:\n",
" h_hat = h_freq\n",
" err_var = 0.0\n",
" else:\n",
" h_hat,err_var = self.channel_estimator(y_rg,no)\n",
"\n",
" # Detection\n",
" if self.det_method == \"mmse-pic\":\n",
" if self._output == \"bit\":\n",
" prior_shape = bits_shape\n",
" elif self._output == \"symbol\":\n",
" # For symbol output, prior needs num_points = 2**num_bits_per_symbol\n",
" prior_shape = list(x.shape) + [2**self.num_bits_per_symbol]\n",
" prior = torch.zeros(prior_shape, device=x.device, dtype=x.dtype)\n",
" det_out = self.detector(y_rg, h_hat, prior, err_var, no)\n",
" else:\n",
" det_out = self.detector(y_rg, h_hat, err_var, no)\n",
"\n",
" # (Decoding) and output\n",
" if self._output == \"bit\":\n",
" llr = det_out.reshape(bits_shape)\n",
" b_hat = self.decoder(llr)\n",
" return b, b_hat\n",
" elif self._output == \"symbol\":\n",
" x_hat = det_out.reshape(x_ind.shape)\n",
" return x_ind, x_hat"
]
},
{
"cell_type": "markdown",
"id": "cce1c898-f431-4613-b6fc-ee702e932ecf",
"metadata": {},
"source": [
"The following function is used to evaluate all of the considered detectors for a given setup: It instantiates the end-to-end systems, runs the simulations, and returns the BER or SER."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "990cefa6-abed-42c6-969c-75bdfdab7946",
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-14T01:15:43.406893Z",
"iopub.status.busy": "2026-02-14T01:15:43.406760Z",
"iopub.status.idle": "2026-02-14T01:15:43.411360Z",
"shell.execute_reply": "2026-02-14T01:15:43.410401Z"
},
"tags": []
},
"outputs": [],
"source": [
"def run_sim(num_tx, num_bits_per_symbol, output, ebno_dbs, perf_csi, det_param=None):\n",
"\n",
" batch_size = 64\n",
"\n",
" lmmse = MIMOOFDMLink(output, \"lmmse\", perf_csi, num_tx, num_bits_per_symbol, det_param)\n",
" lmmse.channel_model.allocate_topology_tensors(batch_size=batch_size, num_bs=1, num_ut=num_tx)\n",
"\n",
" k_best = MIMOOFDMLink(output, \"k-best\", perf_csi, num_tx, num_bits_per_symbol, det_param)\n",
" k_best.channel_model.allocate_topology_tensors(batch_size=batch_size, num_bs=1, num_ut=num_tx)\n",
"\n",
" ep = MIMOOFDMLink(output, \"ep\", perf_csi, num_tx, num_bits_per_symbol, det_param)\n",
" ep.channel_model.allocate_topology_tensors(batch_size=batch_size, num_bs=1, num_ut=num_tx)\n",
"\n",
" mmse_pic = MIMOOFDMLink(output, \"mmse-pic\", perf_csi, num_tx, num_bits_per_symbol, det_param)\n",
" mmse_pic.channel_model.allocate_topology_tensors(batch_size=batch_size, num_bs=1, num_ut=num_tx)\n",
"\n",
" if output == \"symbol\":\n",
" soft_estimates = False\n",
" ylabel = \"Uncoded SER\"\n",
" else:\n",
" soft_estimates = True\n",
" ylabel = \"Coded BER\"\n",
"\n",
" er_lmmse,_ = sim_ber(lmmse,\n",
" ebno_dbs,\n",
" batch_size=64,\n",
" max_mc_iter=500,\n",
" num_target_block_errors=200,\n",
" soft_estimates=soft_estimates,\n",
" compile_mode=\"reduce-overhead\");\n",
"\n",
" er_ep,_ = sim_ber(ep,\n",
" ebno_dbs,\n",
" batch_size=64,\n",
" max_mc_iter=500,\n",
" num_target_block_errors=200,\n",
" soft_estimates=soft_estimates,\n",
" compile_mode=\"reduce-overhead\");\n",
"\n",
" er_kbest,_ = sim_ber(k_best,\n",
" ebno_dbs,\n",
" batch_size=64,\n",
" max_mc_iter=500,\n",
" num_target_block_errors=200,\n",
" soft_estimates=soft_estimates,\n",
" compile_mode=\"reduce-overhead\");\n",
"\n",
" er_mmse_pic,_ = sim_ber(mmse_pic,\n",
" ebno_dbs,\n",
" batch_size=64,\n",
" max_mc_iter=500,\n",
" num_target_block_errors=200,\n",
" soft_estimates=soft_estimates,\n",
" compile_mode=\"reduce-overhead\");\n",
"\n",
" return er_lmmse, er_ep, er_kbest, er_mmse_pic"
]
},
{
"cell_type": "markdown",
"id": "f5b2eb9f-685c-4481-a5c1-a2ce7d132db1",
"metadata": {},
"source": [
"The next cell defines the simulation parameters."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "386ef947-c7db-48be-be7e-ddaade014537",
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-14T01:15:43.413365Z",
"iopub.status.busy": "2026-02-14T01:15:43.413243Z",
"iopub.status.idle": "2026-02-14T01:15:43.415893Z",
"shell.execute_reply": "2026-02-14T01:15:43.415172Z"
}
},
"outputs": [],
"source": [
"# Range of SNR (dB)\n",
"EBN0_DBs = np.linspace(-10., 20.0, 10)\n",
"\n",
"# Number of transmitters\n",
"NUM_TX = 4\n",
"\n",
"# Modulation order (number of bits per symbol)\n",
"NUM_BITS_PER_SYMBOL = 4 # 16-QAM"
]
},
{
"cell_type": "markdown",
"id": "c0f7deac-a797-4ae9-a1e1-de32cb4b4ab2",
"metadata": {},
"source": [
"We start by evaluating the uncoded SER. The next cell runs the simulations with perfect CSI and channel estimation. Results are stored in the `SER` dictionary."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "d8fc9f89-dff2-4a46-b712-3b032aff67bd",
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-14T01:15:43.418342Z",
"iopub.status.busy": "2026-02-14T01:15:43.418224Z",
"iopub.status.idle": "2026-02-14T01:21:50.356794Z",
"shell.execute_reply": "2026-02-14T01:21:50.355681Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.0938e-01 | 1.0000e+00 | 89856 | 147456 | 256 | 256 | 58.8 |reached target block errors\n",
" -6.667 | 4.8939e-01 | 1.0000e+00 | 72164 | 147456 | 256 | 256 | 4.6 |reached target block errors\n",
" -3.333 | 3.7003e-01 | 1.0000e+00 | 54563 | 147456 | 256 | 256 | 0.0 |reached target block errors\n",
" 0.0 | 2.3027e-01 | 9.7266e-01 | 33954 | 147456 | 249 | 256 | 0.0 |reached target block errors\n",
" 3.333 | 1.3139e-01 | 9.2969e-01 | 19374 | 147456 | 238 | 256 | 0.0 |reached target block errors\n",
" 6.667 | 8.5036e-02 | 8.1250e-01 | 12539 | 147456 | 208 | 256 | 0.0 |reached target block errors\n",
" 10.0 | 2.2468e-02 | 5.0977e-01 | 6626 | 294912 | 261 | 512 | 0.0 |reached target block errors\n",
" 13.333 | 7.8419e-03 | 2.7474e-01 | 3469 | 442368 | 211 | 768 | 0.0 |reached target block errors\n",
" 16.667 | 2.5286e-03 | 1.2109e-01 | 2610 | 1032192 | 217 | 1792 | 0.1 |reached target block errors\n",
" 20.0 | 3.2213e-04 | 3.9062e-02 | 950 | 2949120 | 200 | 5120 | 0.2 |reached target block errors\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.1003e-01 | 1.0000e+00 | 89952 | 147456 | 256 | 256 | 42.9 |reached target block errors\n",
" -6.667 | 4.9367e-01 | 1.0000e+00 | 72795 | 147456 | 256 | 256 | 4.1 |reached target block errors\n",
" -3.333 | 3.3316e-01 | 9.9609e-01 | 49126 | 147456 | 255 | 256 | 0.0 |reached target block errors\n",
" 0.0 | 1.5474e-01 | 9.7656e-01 | 22818 | 147456 | 250 | 256 | 0.0 |reached target block errors\n",
" 3.333 | 3.5075e-02 | 8.3203e-01 | 5172 | 147456 | 213 | 256 | 0.0 |reached target block errors\n",
" 6.667 | 6.8563e-03 | 4.6484e-01 | 2022 | 294912 | 238 | 512 | 0.0 |reached target block errors\n",
" 10.0 | 1.7813e-03 | 1.4193e-01 | 1576 | 884736 | 218 | 1536 | 0.1 |reached target block errors\n",
" 13.333 | 3.4900e-04 | 3.0499e-02 | 1338 | 3833856 | 203 | 6656 | 0.4 |reached target block errors\n",
" 16.667 | 5.6554e-05 | 4.5916e-03 | 1426 | 25214976 | 201 | 43776 | 2.7 |reached target block errors\n",
" 20.0 | 9.3180e-06 | 7.1875e-04 | 687 | 73728000 | 92 | 128000 | 7.9 |reached max iterations\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.3132e-01 | 1.0000e+00 | 93092 | 147456 | 256 | 256 | 60.0 |reached target block errors\n",
" -6.667 | 4.8869e-01 | 1.0000e+00 | 72061 | 147456 | 256 | 256 | 4.0 |reached target block errors\n",
" -3.333 | 2.8948e-01 | 1.0000e+00 | 42685 | 147456 | 256 | 256 | 0.0 |reached target block errors\n",
" 0.0 | 9.7331e-02 | 9.7266e-01 | 14352 | 147456 | 249 | 256 | 0.0 |reached target block errors\n",
" 3.333 | 2.8158e-02 | 6.8359e-01 | 8304 | 294912 | 350 | 512 | 0.1 |reached target block errors\n",
" 6.667 | 6.5274e-03 | 2.7734e-01 | 3850 | 589824 | 284 | 1024 | 0.1 |reached target block errors\n",
" 10.0 | 1.6560e-03 | 7.5284e-02 | 2686 | 1622016 | 212 | 2816 | 0.3 |reached target block errors\n",
" 13.333 | 3.1954e-04 | 1.3308e-02 | 2780 | 8699904 | 201 | 15104 | 1.4 |reached target block errors\n",
" 16.667 | 3.9833e-05 | 2.7549e-03 | 1674 | 42024960 | 201 | 72960 | 6.9 |reached target block errors\n",
" 20.0 | 7.2021e-06 | 5.8594e-04 | 531 | 73728000 | 75 | 128000 | 12.2 |reached max iterations\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.1566e-01 | 1.0000e+00 | 90783 | 147456 | 256 | 256 | 42.7 |reached target block errors\n",
" -6.667 | 4.7897e-01 | 1.0000e+00 | 70627 | 147456 | 256 | 256 | 4.1 |reached target block errors\n",
" -3.333 | 3.0542e-01 | 9.9609e-01 | 45036 | 147456 | 255 | 256 | 0.0 |reached target block errors\n",
" 0.0 | 1.6973e-01 | 9.6875e-01 | 25027 | 147456 | 248 | 256 | 0.0 |reached target block errors\n",
" 3.333 | 5.5813e-02 | 8.7109e-01 | 8230 | 147456 | 223 | 256 | 0.0 |reached target block errors\n",
" 6.667 | 1.4530e-02 | 6.0156e-01 | 4285 | 294912 | 308 | 512 | 0.0 |reached target block errors\n",
" 10.0 | 3.2857e-03 | 2.5684e-01 | 1938 | 589824 | 263 | 1024 | 0.1 |reached target block errors\n",
" 13.333 | 3.9334e-04 | 6.1942e-02 | 812 | 2064384 | 222 | 3584 | 0.2 |reached target block errors\n",
" 16.667 | 7.4925e-05 | 9.6009e-03 | 917 | 12238848 | 204 | 21248 | 1.4 |reached target block errors\n",
" 20.0 | 1.1812e-05 | 1.7246e-03 | 789 | 66797568 | 200 | 115968 | 7.5 |reached target block errors\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.5951e-01 | 1.0000e+00 | 97249 | 147456 | 256 | 256 | 48.4 |reached target block errors\n",
" -6.667 | 5.6549e-01 | 1.0000e+00 | 83385 | 147456 | 256 | 256 | 7.0 |reached target block errors\n",
" -3.333 | 4.1471e-01 | 1.0000e+00 | 61152 | 147456 | 256 | 256 | 0.3 |reached target block errors\n",
" 0.0 | 3.0422e-01 | 9.9219e-01 | 44859 | 147456 | 254 | 256 | 0.2 |reached target block errors\n",
" 3.333 | 1.8903e-01 | 9.6484e-01 | 27874 | 147456 | 247 | 256 | 0.2 |reached target block errors\n",
" 6.667 | 8.9945e-02 | 8.2422e-01 | 13263 | 147456 | 211 | 256 | 0.2 |reached target block errors\n",
" 10.0 | 4.1111e-02 | 6.6406e-01 | 12124 | 294912 | 340 | 512 | 0.4 |reached target block errors\n",
" 13.333 | 1.0166e-02 | 4.1602e-01 | 2998 | 294912 | 213 | 512 | 0.4 |reached target block errors\n",
" 16.667 | 6.2188e-03 | 2.6367e-01 | 3668 | 589824 | 270 | 1024 | 0.8 |reached target block errors\n",
" 20.0 | 4.9262e-03 | 1.6562e-01 | 3632 | 737280 | 212 | 1280 | 1.0 |reached target block errors\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.7356e-01 | 1.0000e+00 | 99320 | 147456 | 256 | 256 | 7.0 |reached target block errors\n",
" -6.667 | 5.4064e-01 | 1.0000e+00 | 79721 | 147456 | 256 | 256 | 0.3 |reached target block errors\n",
" -3.333 | 3.7639e-01 | 9.9609e-01 | 55501 | 147456 | 255 | 256 | 0.2 |reached target block errors\n",
" 0.0 | 2.2509e-01 | 9.9219e-01 | 33191 | 147456 | 254 | 256 | 0.2 |reached target block errors\n",
" 3.333 | 9.5011e-02 | 9.0234e-01 | 14010 | 147456 | 231 | 256 | 0.2 |reached target block errors\n",
" 6.667 | 2.2664e-02 | 6.8750e-01 | 6684 | 294912 | 352 | 512 | 0.4 |reached target block errors\n",
" 10.0 | 6.8925e-03 | 3.4635e-01 | 3049 | 442368 | 266 | 768 | 0.6 |reached target block errors\n",
" 13.333 | 1.7938e-03 | 1.4258e-01 | 1587 | 884736 | 219 | 1536 | 1.3 |reached target block errors\n",
" 16.667 | 1.0265e-03 | 7.4574e-02 | 1665 | 1622016 | 210 | 2816 | 2.3 |reached target block errors\n",
" 20.0 | 1.4254e-03 | 5.0781e-02 | 3363 | 2359296 | 208 | 4096 | 3.4 |reached target block errors\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.7143e-01 | 1.0000e+00 | 99006 | 147456 | 256 | 256 | 3.1 |reached target block errors\n",
" -6.667 | 5.4446e-01 | 1.0000e+00 | 80284 | 147456 | 256 | 256 | 0.3 |reached target block errors\n",
" -3.333 | 3.8318e-01 | 1.0000e+00 | 56502 | 147456 | 256 | 256 | 0.2 |reached target block errors\n",
" 0.0 | 2.0933e-01 | 9.8047e-01 | 30867 | 147456 | 251 | 256 | 0.2 |reached target block errors\n",
" 3.333 | 7.9814e-02 | 8.6719e-01 | 11769 | 147456 | 222 | 256 | 0.2 |reached target block errors\n",
" 6.667 | 1.8456e-02 | 5.6055e-01 | 5443 | 294912 | 287 | 512 | 0.4 |reached target block errors\n",
" 10.0 | 5.8594e-03 | 2.6465e-01 | 3456 | 589824 | 271 | 1024 | 0.9 |reached target block errors\n",
" 13.333 | 2.7379e-03 | 1.2388e-01 | 2826 | 1032192 | 222 | 1792 | 1.5 |reached target block errors\n",
" 16.667 | 7.2758e-04 | 6.0268e-02 | 1502 | 2064384 | 216 | 3584 | 3.1 |reached target block errors\n",
" 20.0 | 1.3877e-03 | 5.0537e-02 | 3274 | 2359296 | 207 | 4096 | 3.5 |reached target block errors\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 6.4423e-01 | 1.0000e+00 | 94995 | 147456 | 256 | 256 | 7.4 |reached target block errors\n",
" -6.667 | 5.4351e-01 | 1.0000e+00 | 80144 | 147456 | 256 | 256 | 0.3 |reached target block errors\n",
" -3.333 | 3.9070e-01 | 1.0000e+00 | 57611 | 147456 | 256 | 256 | 0.2 |reached target block errors\n",
" 0.0 | 2.4167e-01 | 9.9219e-01 | 35635 | 147456 | 254 | 256 | 0.2 |reached target block errors\n",
" 3.333 | 7.9400e-02 | 9.4141e-01 | 11708 | 147456 | 241 | 256 | 0.2 |reached target block errors\n",
" 6.667 | 3.3023e-02 | 7.0312e-01 | 9739 | 294912 | 360 | 512 | 0.4 |reached target block errors\n",
" 10.0 | 1.0522e-02 | 4.7656e-01 | 3103 | 294912 | 244 | 512 | 0.4 |reached target block errors\n",
" 13.333 | 2.2397e-03 | 2.0117e-01 | 1321 | 589824 | 206 | 1024 | 0.8 |reached target block errors\n",
" 16.667 | 1.5304e-03 | 9.0278e-02 | 2031 | 1327104 | 208 | 2304 | 1.9 |reached target block errors\n",
" 20.0 | 1.2963e-03 | 6.1599e-02 | 2485 | 1916928 | 205 | 3328 | 2.8 |reached target block errors\n"
]
}
],
"source": [
"SER = {} # Store the results\n",
"\n",
"# Perfect CSI\n",
"ser_lmmse, ser_ep, ser_kbest, ser_mmse_pic = run_sim(NUM_TX, NUM_BITS_PER_SYMBOL, \"symbol\", EBN0_DBs, True)\n",
"SER['Perf. CSI / LMMSE'] = ser_lmmse\n",
"SER['Perf. CSI / EP'] = ser_ep\n",
"SER['Perf. CSI / K-Best'] = ser_kbest\n",
"SER['Perf. CSI / MMSE-PIC'] = ser_mmse_pic\n",
"\n",
"# Imperfect CSI\n",
"ser_lmmse, ser_ep, ser_kbest, ser_mmse_pic = run_sim(NUM_TX, NUM_BITS_PER_SYMBOL, \"symbol\", EBN0_DBs, False)\n",
"SER['Ch. Est. / LMMSE'] = ser_lmmse\n",
"SER['Ch. Est. / EP'] = ser_ep\n",
"SER['Ch. Est. / K-Best'] = ser_kbest\n",
"SER['Ch. Est. / MMSE-PIC'] = ser_mmse_pic"
]
},
{
"cell_type": "markdown",
"id": "a7b436e3-1fc4-4c6a-b2b3-338cf7158851",
"metadata": {},
"source": [
"Next, we evaluate the coded BER. The cell below runs the simulations with perfect CSI and channel estimation. Results are stored in the `BER` dictionary."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "56dda021-51c5-4c23-8675-e9e08e556c4f",
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-14T01:21:50.358999Z",
"iopub.status.busy": "2026-02-14T01:21:50.358689Z",
"iopub.status.idle": "2026-02-14T01:51:29.191456Z",
"shell.execute_reply": "2026-02-14T01:51:29.190334Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 1.8763e-01 | 8.5156e-01 | 55335 | 294912 | 218 | 256 | 46.3 |reached target block errors\n",
" -6.667 | 1.1853e-01 | 6.4844e-01 | 69914 | 589824 | 332 | 512 | 5.6 |reached target block errors\n",
" -3.333 | 5.1604e-02 | 3.1771e-01 | 45656 | 884736 | 244 | 768 | 0.1 |reached target block errors\n",
" 0.0 | 2.1561e-02 | 1.4453e-01 | 38151 | 1769472 | 222 | 1536 | 0.1 |reached target block errors\n",
" 3.333 | 6.2679e-03 | 4.6415e-02 | 31424 | 5013504 | 202 | 4352 | 0.3 |reached target block errors\n",
" 6.667 | 1.3272e-03 | 9.8892e-03 | 30921 | 23298048 | 200 | 20224 | 1.3 |reached target block errors\n",
" 10.0 | 3.2107e-04 | 2.6305e-03 | 28122 | 87588864 | 200 | 76032 | 4.8 |reached target block errors\n",
" 13.333 | 4.6115e-05 | 4.0625e-04 | 6800 | 147456000 | 52 | 128000 | 8.0 |reached max iterations\n",
" 16.667 | 5.7170e-06 | 4.6875e-05 | 843 | 147456000 | 6 | 128000 | 8.1 |reached max iterations\n",
" 20.0 | 6.7817e-08 | 7.8125e-06 | 10 | 147456000 | 1 | 128000 | 8.1 |reached max iterations\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 1.6694e-01 | 7.8125e-01 | 49232 | 294912 | 200 | 256 | 48.3 |reached target block errors\n",
" -6.667 | 9.6598e-02 | 5.4102e-01 | 56976 | 589824 | 277 | 512 | 6.9 |reached target block errors\n",
" -3.333 | 3.4877e-02 | 2.4609e-01 | 41143 | 1179648 | 252 | 1024 | 0.1 |reached target block errors\n",
" 0.0 | 7.2706e-03 | 5.5729e-02 | 32163 | 4423680 | 214 | 3840 | 0.3 |reached target block errors\n",
" 3.333 | 7.3176e-04 | 5.9177e-03 | 28918 | 39518208 | 203 | 34304 | 2.9 |reached target block errors\n",
" 6.667 | 1.1944e-04 | 9.4531e-04 | 17612 | 147456000 | 121 | 128000 | 10.9 |reached max iterations\n",
" 10.0 | 1.0722e-05 | 8.5937e-05 | 1581 | 147456000 | 11 | 128000 | 10.9 |reached max iterations\n",
" 13.333 | 8.1380e-07 | 7.8125e-06 | 120 | 147456000 | 1 | 128000 | 10.9 |reached max iterations\n",
" 16.667 | 1.4242e-07 | 7.8125e-06 | 21 | 147456000 | 1 | 128000 | 11.0 |reached max iterations\n",
" 20.0 | 0.0000e+00 | 0.0000e+00 | 0 | 147456000 | 0 | 128000 | 11.0 |reached max iterations\n",
"\n",
"Simulation stopped as no error occurred @ EbNo = 20.0 dB.\n",
"\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 2.0430e-01 | 9.2578e-01 | 60250 | 294912 | 237 | 256 | 45.8 |reached target block errors\n",
" -6.667 | 1.1262e-01 | 6.3086e-01 | 66427 | 589824 | 323 | 512 | 4.5 |reached target block errors\n",
" -3.333 | 3.7238e-02 | 2.4609e-01 | 43928 | 1179648 | 252 | 1024 | 0.1 |reached target block errors\n",
" 0.0 | 4.9732e-03 | 3.7574e-02 | 30800 | 6193152 | 202 | 5376 | 0.7 |reached target block errors\n",
" 3.333 | 7.1578e-04 | 5.1238e-03 | 32508 | 45416448 | 202 | 39424 | 5.0 |reached target block errors\n",
" 6.667 | 8.6982e-05 | 6.4063e-04 | 12826 | 147456000 | 82 | 128000 | 16.3 |reached max iterations\n",
" 10.0 | 1.3577e-05 | 1.3281e-04 | 2002 | 147456000 | 17 | 128000 | 16.4 |reached max iterations\n",
" 13.333 | 9.3587e-07 | 7.8125e-06 | 138 | 147456000 | 1 | 128000 | 16.4 |reached max iterations\n",
" 16.667 | 0.0000e+00 | 0.0000e+00 | 0 | 147456000 | 0 | 128000 | 16.4 |reached max iterations\n",
"\n",
"Simulation stopped as no error occurred @ EbNo = 16.7 dB.\n",
"\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 1.9384e-01 | 8.6328e-01 | 57167 | 294912 | 221 | 256 | 51.1 |reached target block errors\n",
" -6.667 | 1.1594e-01 | 6.1719e-01 | 68382 | 589824 | 316 | 512 | 4.8 |reached target block errors\n",
" -3.333 | 4.8464e-02 | 3.0469e-01 | 42878 | 884736 | 234 | 768 | 0.1 |reached target block errors\n",
" 0.0 | 1.1913e-02 | 9.0712e-02 | 31619 | 2654208 | 209 | 2304 | 0.2 |reached target block errors\n",
" 3.333 | 1.5803e-03 | 1.6276e-02 | 22371 | 14155776 | 200 | 12288 | 1.0 |reached target block errors\n",
" 6.667 | 1.7648e-04 | 2.0505e-03 | 19830 | 112361472 | 200 | 97536 | 8.1 |reached target block errors\n",
" 10.0 | 2.0589e-05 | 2.1094e-04 | 3036 | 147456000 | 27 | 128000 | 10.6 |reached max iterations\n",
" 13.333 | 1.3563e-08 | 7.8125e-06 | 2 | 147456000 | 1 | 128000 | 10.6 |reached max iterations\n",
" 16.667 | 0.0000e+00 | 0.0000e+00 | 0 | 147456000 | 0 | 128000 | 10.6 |reached max iterations\n",
"\n",
"Simulation stopped as no error occurred @ EbNo = 16.7 dB.\n",
"\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 2.0864e-01 | 9.0234e-01 | 61529 | 294912 | 231 | 256 | 51.3 |reached target block errors\n",
" -6.667 | 1.4157e-01 | 6.9531e-01 | 83499 | 589824 | 356 | 512 | 6.0 |reached target block errors\n",
" -3.333 | 7.9870e-02 | 4.4531e-01 | 47109 | 589824 | 228 | 512 | 0.4 |reached target block errors\n",
" 0.0 | 3.8957e-02 | 2.3926e-01 | 45956 | 1179648 | 245 | 1024 | 0.9 |reached target block errors\n",
" 3.333 | 1.3526e-02 | 8.8542e-02 | 35900 | 2654208 | 204 | 2304 | 1.9 |reached target block errors\n",
" 6.667 | 4.4031e-03 | 3.0048e-02 | 33762 | 7667712 | 200 | 6656 | 5.5 |reached target block errors\n",
" 10.0 | 1.5416e-03 | 1.0248e-02 | 35008 | 22708224 | 202 | 19712 | 16.4 |reached target block errors\n",
" 13.333 | 4.9725e-04 | 3.3435e-03 | 34608 | 69599232 | 202 | 60416 | 50.3 |reached target block errors\n",
" 16.667 | 3.0654e-04 | 2.0187e-03 | 34986 | 114130944 | 200 | 99072 | 82.5 |reached target block errors\n",
" 20.0 | 2.7137e-04 | 2.2337e-03 | 28411 | 104693760 | 203 | 90880 | 75.6 |reached target block errors\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 2.1326e-01 | 9.2188e-01 | 62894 | 294912 | 236 | 256 | 16.6 |reached target block errors\n",
" -6.667 | 1.3167e-01 | 6.7773e-01 | 77664 | 589824 | 347 | 512 | 0.5 |reached target block errors\n",
" -3.333 | 5.6656e-02 | 3.5286e-01 | 50126 | 884736 | 271 | 768 | 0.7 |reached target block errors\n",
" 0.0 | 1.7187e-02 | 1.2277e-01 | 35480 | 2064384 | 220 | 1792 | 1.5 |reached target block errors\n",
" 3.333 | 4.2129e-03 | 3.0048e-02 | 32303 | 7667712 | 200 | 6656 | 5.7 |reached target block errors\n",
" 6.667 | 1.1124e-03 | 8.3943e-03 | 30837 | 27721728 | 202 | 24064 | 20.6 |reached target block errors\n",
" 10.0 | 5.5844e-04 | 4.3163e-03 | 29809 | 53379072 | 200 | 46336 | 39.6 |reached target block errors\n",
" 13.333 | 3.5832e-04 | 2.6847e-03 | 30751 | 85819392 | 200 | 74496 | 63.7 |reached target block errors\n",
" 16.667 | 1.9324e-04 | 1.6155e-03 | 27697 | 143327232 | 201 | 124416 | 106.4 |reached target block errors\n",
" 20.0 | 1.7403e-04 | 1.4375e-03 | 25662 | 147456000 | 184 | 128000 | 109.5 |reached max iterations\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 2.4421e-01 | 9.7266e-01 | 72021 | 294912 | 249 | 256 | 11.2 |reached target block errors\n",
" -6.667 | 1.5177e-01 | 7.5781e-01 | 89520 | 589824 | 388 | 512 | 0.5 |reached target block errors\n",
" -3.333 | 7.2267e-02 | 4.0234e-01 | 42625 | 589824 | 206 | 512 | 0.5 |reached target block errors\n",
" 0.0 | 1.6280e-02 | 1.0547e-01 | 38410 | 2359296 | 216 | 2048 | 1.8 |reached target block errors\n",
" 3.333 | 3.1813e-03 | 2.0662e-02 | 35652 | 11206656 | 201 | 9728 | 8.7 |reached target block errors\n",
" 6.667 | 1.1505e-03 | 6.6636e-03 | 40375 | 35094528 | 203 | 30464 | 27.2 |reached target block errors\n",
" 10.0 | 4.1301e-04 | 2.7168e-03 | 35201 | 85229568 | 201 | 73984 | 66.2 |reached target block errors\n",
" 13.333 | 2.7698e-04 | 2.2132e-03 | 28835 | 104103936 | 200 | 90368 | 80.8 |reached target block errors\n",
" 16.667 | 2.0795e-04 | 1.5991e-03 | 30111 | 144801792 | 201 | 125696 | 112.4 |reached target block errors\n",
" 20.0 | 1.9234e-04 | 1.4375e-03 | 28362 | 147456000 | 184 | 128000 | 114.5 |reached max iterations\n",
"EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n",
"---------------------------------------------------------------------------------------------------------------------------------------\n",
" -10.0 | 2.1293e-01 | 9.1016e-01 | 62795 | 294912 | 233 | 256 | 14.3 |reached target block errors\n",
" -6.667 | 1.5255e-01 | 7.2070e-01 | 89977 | 589824 | 369 | 512 | 0.5 |reached target block errors\n",
" -3.333 | 7.5138e-02 | 4.3164e-01 | 44318 | 589824 | 221 | 512 | 0.4 |reached target block errors\n",
" 0.0 | 3.0155e-02 | 1.9531e-01 | 44465 | 1474560 | 250 | 1280 | 1.1 |reached target block errors\n",
" 3.333 | 6.3452e-03 | 5.2604e-02 | 28069 | 4423680 | 202 | 3840 | 3.3 |reached target block errors\n",
" 6.667 | 1.4153e-03 | 1.3605e-02 | 24209 | 17104896 | 202 | 14848 | 12.6 |reached target block errors\n",
" 10.0 | 5.5931e-04 | 5.2773e-03 | 24907 | 44531712 | 204 | 38656 | 32.8 |reached target block errors\n",
" 13.333 | 3.5451e-04 | 3.0637e-03 | 26660 | 75202560 | 200 | 65280 | 55.4 |reached target block errors\n",
" 16.667 | 2.4626e-04 | 2.2627e-03 | 25201 | 102334464 | 201 | 88832 | 75.4 |reached target block errors\n",
" 20.0 | 2.0771e-04 | 1.9435e-03 | 24870 | 119734272 | 202 | 103936 | 88.3 |reached target block errors\n"
]
}
],
"source": [
"BER = {} # Store the results\n",
"\n",
"# Perfect CSI\n",
"ber_lmmse, ber_ep, ber_kbest, ber_mmse_pic = run_sim(NUM_TX, NUM_BITS_PER_SYMBOL, \"bit\", EBN0_DBs, True)\n",
"BER['Perf. CSI / LMMSE'] = ber_lmmse\n",
"BER['Perf. CSI / EP'] = ber_ep\n",
"BER['Perf. CSI / K-Best'] = ber_kbest\n",
"BER['Perf. CSI / MMSE-PIC'] = ber_mmse_pic\n",
"\n",
"# Imperfect CSI\n",
"ber_lmmse, ber_ep, ber_kbest, ber_mmse_pic = run_sim(NUM_TX, NUM_BITS_PER_SYMBOL, \"bit\", EBN0_DBs, False)\n",
"BER['Ch. Est. / LMMSE'] = ber_lmmse\n",
"BER['Ch. Est. / EP'] = ber_ep\n",
"BER['Ch. Est. / K-Best'] = ber_kbest\n",
"BER['Ch. Est. / MMSE-PIC'] = ber_mmse_pic"
]
},
{
"cell_type": "markdown",
"id": "2755232f-5704-4246-9fc2-c08304f8cf5a",
"metadata": {},
"source": [
"Finally, we plot the results."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "818ccd65-02ab-4636-9cf5-887862ec978d",
"metadata": {
"execution": {
"iopub.execute_input": "2026-02-14T01:51:29.194143Z",
"iopub.status.busy": "2026-02-14T01:51:29.194003Z",
"iopub.status.idle": "2026-02-14T01:51:29.659771Z",
"shell.execute_reply": "2026-02-14T01:51:29.658797Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for k in SER.keys():\n",
" SER[k] = SER[k].cpu().numpy()\n",
"for k in BER.keys():\n",
" BER[k] = BER[k].cpu().numpy()\n",
"\n",
"fig, ax = plt.subplots(1,2, figsize=(16,7))\n",
"fig.suptitle(f\"{NUM_TX}x{NUM_RX_ANT} UMi | {2**NUM_BITS_PER_SYMBOL}-QAM\")\n",
"\n",
"## SER\n",
"\n",
"ax[0].set_title(\"Symbol error rate\")\n",
"# Perfect CSI\n",
"ax[0].semilogy(EBN0_DBs, SER['Perf. CSI / LMMSE'], 'x-', label='Perf. CSI / LMMSE', c='C0')\n",
"ax[0].semilogy(EBN0_DBs, SER['Perf. CSI / EP'], 'o--', label='Perf. CSI / EP', c='C0')\n",
"ax[0].semilogy(EBN0_DBs, SER['Perf. CSI / K-Best'], 's-.', label='Perf. CSI / K-Best', c='C0')\n",
"ax[0].semilogy(EBN0_DBs, SER['Perf. CSI / MMSE-PIC'], 'd:', label='Perf. CSI / MMSE-PIC', c='C0')\n",
"\n",
"# Imperfect CSI\n",
"ax[0].semilogy(EBN0_DBs, SER['Ch. Est. / LMMSE'], 'x-', label='Ch. Est. / LMMSE', c='C1')\n",
"ax[0].semilogy(EBN0_DBs, SER['Ch. Est. / EP'], 'o--', label='Ch. Est. / EP', c='C1')\n",
"ax[0].semilogy(EBN0_DBs, SER['Ch. Est. / K-Best'], 's-.', label='Ch. Est. / K-Best', c='C1')\n",
"ax[0].semilogy(EBN0_DBs, SER['Ch. Est. / MMSE-PIC'], 'd:', label='Ch. Est. / MMSE-PIC', c='C1')\n",
"\n",
"ax[0].set_xlabel(r\"$E_b/N0$\")\n",
"ax[0].set_ylabel(\"SER\")\n",
"ax[0].set_ylim((1e-4, 1.0))\n",
"ax[0].legend()\n",
"ax[0].grid(True)\n",
"\n",
"## SER\n",
"\n",
"ax[1].set_title(\"Bit error rate\")\n",
"# Perfect CSI\n",
"ax[1].semilogy(EBN0_DBs, BER['Perf. CSI / LMMSE'], 'x-', label='Perf. CSI / LMMSE', c='C0')\n",
"ax[1].semilogy(EBN0_DBs, BER['Perf. CSI / EP'], 'o--', label='Perf. CSI / EP', c='C0')\n",
"ax[1].semilogy(EBN0_DBs, BER['Perf. CSI / K-Best'], 's-.', label='Perf. CSI / K-Best', c='C0')\n",
"ax[1].semilogy(EBN0_DBs, BER['Perf. CSI / MMSE-PIC'], 'd:', label='Perf. CSI / MMSE-PIC', c='C0')\n",
"\n",
"# Imperfect CSI\n",
"ax[1].semilogy(EBN0_DBs, BER['Ch. Est. / LMMSE'], 'x-', label='Ch. Est. / LMMSE', c='C1')\n",
"ax[1].semilogy(EBN0_DBs, BER['Ch. Est. / EP'], 'o--', label='Ch. Est. / EP', c='C1')\n",
"ax[1].semilogy(EBN0_DBs, BER['Ch. Est. / K-Best'], 's-.', label='Ch. Est. / K-Best', c='C1')\n",
"ax[1].semilogy(EBN0_DBs, BER['Ch. Est. / MMSE-PIC'], 'd:', label='Ch. Est. / MMSE-PIC', c='C1')\n",
"\n",
"ax[1].set_xlabel(r\"$E_b/N0$\")\n",
"ax[1].set_ylabel(\"BER\")\n",
"ax[1].set_ylim((1e-4, 1.0))\n",
"ax[1].legend()\n",
"ax[1].grid(True)"
]
},
{
"cell_type": "markdown",
"id": "aedbd5e6-c779-48f1-96b0-8ca604d726a2",
"metadata": {},
"source": [
"For this setup, the non-linear detection algorithms K-Best, EP, and MMSE-PIC, outperform the linear MMSE detection method.\n",
"It is remarkable that K-Best and EP with imperfect CSI achieve lower BER than LMMSE detection with perfect CSI.\n",
"\n",
"However, one should keep in mind that:\n",
"\n",
"- EP is prone to numerical imprecision and could therefore achieve better BER/SER with double precision (`dtype=torch.complex128`). The number of iterations `l` as well as the update smoothing parameter `beta` impact performance.\n",
"\n",
"- For K-Best, there is not a unique way to compute soft information and better performance could be achieved with improved methods for computing soft information from a list of candidates (see [list2llr](https://nvlabs.github.io/sionna/phy/api/mimo/sionna.phy.mimo.List2LLR.html)). Increasing the list size `k` results in improved accuracy at the cost of higher complexity.\n",
"\n",
"- MMSE-PIC can be easily combined with a decoder to implement iterative detection and decoding, as it takes as input soft prior information on the bits/symbols."
]
},
{
"cell_type": "markdown",
"id": "15b1db2a",
"metadata": {},
"source": []
}
],
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"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
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"name": "python",
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