### Basic Sionna Setup and Usage

Source: https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Sionna_tutorial_part1.ipynb

Demonstrates the fundamental setup of Sionna, including importing necessary modules and creating a basic simulation environment. This is useful for getting started with the library.

```python
import sionna
from sionna.rt import LayerIndoors, IndoorChannel
from sionna.ofdm import ResourceGrid, OFDMSymbolsInfo, OFDMModulation
from sionna.mapping import Mapper, Demapper
from sionna.utils import BinarySource, SionnaConfig

# Sionna configuration
config = SionnaConfig()
config.parse("config.ini")

# Resource grid definition
num_ofdm_symbols = 14
subcarrier_spacing = 15  # kHz
num_resource_blocks = 52

# Create a resource grid
resource_grid = ResourceGrid(num_ofdm_symbols=num_ofdm_symbols,
                            subcarrier_spacing=subcarrier_spacing,
                            num_resource_blocks=num_resource_blocks)

# OFDM symbols information
ofdm_info = OFDMSymbolsInfo(num_ofdm_symbols=num_ofdm_symbols,
                            subcarrier_spacing=subcarrier_spacing,
                            num_resource_blocks=num_resource_blocks)

# OFDM modulation
ofdm_modulation = OFDMModulation(ofdm_info=ofdm_info)

# Mapper and Demapper
mapper = Mapper(k=2,  # Number of bits per symbol
                modulation_type="QAM")
demapper = Demapper(k=2,
                    modulation_type="QAM",
                    log_likelihood_ratio=True)

# Binary source
num_bits_source = 10000
binary_source = BinarySource(size=num_bits_source)

# Indoor channel model
# Define a simple indoor environment
# This is a placeholder and would typically be loaded from a file or defined more elaborately
# For demonstration, we'll use a simplified setup
# layer_indoors = LayerIndoors()
# indoor_channel = IndoorChannel(layer_indoors=layer_indoors)

print("Sionna setup complete.")
print(f"Resource Grid: {resource_grid}")
print(f"OFDM Info: {ofdm_info}")

```

--------------------------------

### Basic Sionna Setup

Source: https://nvlabs.github.io/sionna/_sources/phy/tutorials/notebooks/Sionna_tutorial_part1.ipynb.txt

This snippet shows the basic imports and setup required to start using Sionna for simulations. Ensure Sionna is installed before running.

```python
import sionna
import numpy as np
import tensorflow as tf

# Sionna setup
sionna.config.set_log_level(log_level='INFO')

# TensorFlow setup
tf.random.set_seed(1)
np.random.seed(1)

print(f'Sionna version: {sionna.__version__}')
print(f'TensorFlow version: {tf.__version__}')
```

--------------------------------

### Full System Configuration Example

Source: https://nvlabs.github.io/sionna/_sources/sys/tutorials/notebooks/SYS_Meets_RT.ipynb.txt

A comprehensive example showing the setup of a complete wireless system.

```python
# Full system setup
sys = rt.System(...)
tx = rt.Transmitter(...)
rx = rt.Receiver(...)
channel = rt.RayTracingChannel(...)
sys.add(tx)
sys.add(rx)
sys.add(channel)
tx.antenna_array = rt.AntennaArray(...)
rx.antenna_array = rt.AntennaArray(...)
input_bits = tf.random.uniform(...)
sim_output = sys(input_bits)

```

--------------------------------

### Start System Script Examples

Source: https://nvlabs.github.io/sionna/rk/scripts/start-system.html

Examples demonstrating how to run the start-system.sh script with and without a specific configuration name.

```bash
start-system.sh
```

```bash
start-system.sh rfsim
```

```bash
start-system.sh b200
```

--------------------------------

### Basic Sionna Setup

Source: https://nvlabs.github.io/sionna/_sources/phy/tutorials/notebooks/Sionna_tutorial_part1.ipynb.txt

Initializes Sionna and sets up basic parameters for a simulation. This is a common starting point for many Sionna examples.

```python
import sionna
from sionna.rt import MapLayer, Map, Camera, Sensor, Scene, Body, Material, Transform, PointCloud
from sionna.utils import Bilal, Plot
from sionna.utils.files import download_file
import numpy as np
import open3d as o3d
import os

# Set the Sionna log level to INFO
sionna.config.log_level = "INFO"

# Define the path to the dataset
dataset_path = "./data"

# Create the dataset directory if it does not exist
os.makedirs(dataset_path, exist_ok=True)

# Download the dataset if it does not exist
if not os.path.exists(os.path.join(dataset_path, "map_layer.png")):
    download_file(
        url="https://github.com/tf-encrypted/tf-encrypted/raw/master/examples/data/map_layer.png",
        save_path=dataset_path,
        show_progress=True,
    )

# Load the map layer
map_layer = MapLayer(os.path.join(dataset_path, "map_layer.png"))

# Create a map object
map_obj = Map(map_layer)

# Create a scene object
scene = Scene(map_obj=map_obj)

```

--------------------------------

### Example with Specific Parameters

Source: https://nvlabs.github.io/sionna/_sources/sys/tutorials/notebooks/End-to-End_Example.ipynb.txt

A concrete example demonstrating the setup and execution with specific simulation parameters.

```python
# Example parameters
num_rays = 5000
max_depth = 5
frequency = 2.4e9 # 2.4 GHz

scene.frequency = frequency
cir = scene.ray_tracing(num_rays=num_rays, max_depth=max_depth).get_cir("rx_1")

plt.figure()
plt.stem(cir.delay_bins * 1e9, np.abs(cir.cir))
plt.xlabel("Delay (ns)")
plt.ylabel("Amplitude")
plt.title(f"CIR at {frequency/1e9} GHz")
plt.show()
```

--------------------------------

### Scattering Simulation Setup Example

Source: https://nvlabs.github.io/sionna/_sources/rt/tutorials/Scattering.ipynb.txt

Example of setting up a scattering simulation.

```python
def setup_scattering_simulation_example(sim_type):
    print(f"Setting up {sim_type} simulation.")
    # Initialize simulation parameters
    return "Setup complete."
```

--------------------------------

### Basic Sionna Setup

Source: https://nvlabs.github.io/sionna/_sources/rt/tutorials/Introduction.ipynb.txt

Demonstrates the basic setup for using Sionna, including necessary imports. This is a starting point for most Sionna applications.

```python
import sionna
import numpy as np
import tensorflow as tf

# Example usage (not functional without further definitions)
# num_bits = 1000
# x = np.random.randint(0, 2, num_bits)
# print(x)
```

--------------------------------

### Basic Sionna Setup

Source: https://nvlabs.github.io/sionna/sys/tutorials/notebooks/End-to-End_Example.ipynb

Initializes Sionna and sets up basic parameters for a communication system simulation. Requires Sionna to be installed.

```python
import sionna
from sionna.rt import ChannelScene, Camera, PlanarArray, Point, Scene, Sensor, Transmitter, Receiver, Material, Sky
from sionna.utils import Plotting, Config
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
from scipy.spatial.transform import Rotation as R

# Configure Sionna
# Config.root = "/path/to/sionna/"
# Config.log_level = "INFO"
# Config.gpu_allow_growth = True

# Create a ChannelScene
scene = ChannelScene(name="scene")

# Add a transmitter
tx = Transmitter(name="tx")
scene.add(tx)

# Add a receiver
rx = Receiver(name="rx")
scene.add(rx)

# Add a camera
cam = Camera(name="cam")
scene.add(cam)

# Add a sensor
sensor = Sensor(name="sensor")
scene.add(sensor)

# Add a planar array
pa = PlanarArray(name="pa")
scene.add(pa)

# Add a point
point = Point(name="point")
scene.add(point)

# Add a sky
sky = Sky(name="sky")
scene.add(sky)

# Add a material
material = Material(name="material")
scene.add(material)

# Print scene information
print(scene.info())
```

--------------------------------

### OFDM MIMO System Setup

Source: https://nvlabs.github.io/sionna/phy/tutorials/notebooks/OFDM_MIMO_Detection.ipynb

Sets up a basic OFDM MIMO system with specified parameters. Ensure Sionna is installed and imported.

```python
import numpy as np
import tensorflow as tf
from sionna.rt import LayerlessScenario, Transmitter, Receiver
from sionna.ofdm import * # noqa
from sionna.mimo import * # noqa
from sionna.channel import * # noqa
from sionna.utils import * # noqa
from sionna.training import * # noqa
from sionna.constants import * # noqa

# System parameters
num_rx = 2
num_tx = 2
num_ofdm_symbols = 10
fft_size = 64
subcarrier_spacing = 15
num_data_subcarriers = 52
num_pilot_subcarriers = 4
num_bits = 1
num_users = 1

# Create a LayerlessScenario
scenario = LayerlessScenario()

tx = Transmitter(scenario=scenario, num_antennas=num_tx)
rx = Receiver(scenario=scenario, num_antennas=num_rx)

# Create an OFDMModem
ofdm_modem = OFDMModem(fft_size=fft_size, 
                       shift_subcarriers=True, 
                       num_data_subcarriers=num_data_subcarriers, 
                       num_pilot_subcarriers=num_pilot_subcarriers, 
                       pilot_pattern='dh', 
                       pilot_subcarrier_spacing=num_data_subcarriers//num_pilot_subcarriers, 
                       pilot_subcarriers_offset=0, 
                       pilot_value=1+1j, 
                       num_bits_per_symbol=num_bits)

# Create a MIMOChannel
# The channel is created on the fly in the simulation loop
```

--------------------------------

### Channel Modeling Example

Source: https://nvlabs.github.io/sionna/sys/tutorials/notebooks/End-to-End_Example.ipynb

Demonstrates channel modeling with Sionna. This requires a scene and a transmitter/receiver setup.

```python
from sionna.rt import Scene, Camera, PlanarArray
from sionna.channel import Dരുവനന്തപുരം
from sionna.utils import Plot
import numpy as np

# Create a scene
scene = Scene()

# Add a camera
camera = Camera(name="camera", 
                position=[0,0,0], 
                look_at=[1,0,0], 
                up=[0,0,1], 
                near_clip=0.1, 
                far_clip=100.0, 
                fov=90.0)
scene.add_camera(camera)

# Add a planar array
array = PlanarArray(name="array", 
                    position=[0,0,0], 
                    orientation=[1,0,0,0], 
                    num_rows=1, 
                    num_cols=1, 
                    element_spacing=[0.5, 0.5], 
                    polarization="dual rx")
scene.add_object(array)

# Create a channel
channel = Dരുവനന്തപുരം()

# Simulate channel
# This part would typically involve defining a transmitter and receiver
# and then calling channel.sample(..., ...)

# Plot the scene (optional)
plot = Plot()
plot.plot_scene(scene)
```

--------------------------------

### OFDM MIMO System Setup

Source: https://nvlabs.github.io/sionna/phy/tutorials/notebooks/OFDM_MIMO_Detection.ipynb

Sets up a basic OFDM MIMO system with specified parameters. Ensure Sionna is installed and configured.

```python
import numpy as np
import tensorflow as tf
from sionna.rt import LayerPath, Camera, Scene, Transmitter, Receiver
from sionna.ofdm import * 
from sionna.mimo import * 
from sionna.channel import * 
from sionna.utils import * 
from sionna.training import * 
from sionna.constants import * 
from sionna.utils.plot import plot_scatter_diagram

# System parameters
num_rx = 2
num_tx = 2
n_fft = 64
num_ofdm_symbols = 14
subcarrier_spacing = 15e3

# Channel parameters
# For simplicity, we use a flat fading channel model here.
# For more complex scenarios, consider using sionna.rt for ray tracing.
# For this example, we use a simple AWGN channel.

# Define the OFDM system
ofdm_mod = OFDMModulation(fft_size=n_fft, 
                        num_ofdm_symbols=num_ofdm_symbols, 
                        subcarrier_spacing=subcarrier_spacing, 
                        pilot_pattern='dpd', 
                        pilot_re=np.arange(1, n_fft, 2), 
                        pilot_re_len=1)

# Define the MIMO channel
# For this example, we use a simple AWGN channel.
# For more complex scenarios, consider using sionna.rt for ray tracing.
# For this example, we use a simple AWGN channel.

# Define the MIMO detector
# This will be defined later based on the chosen algorithm.
```

--------------------------------

### Run quickstart-cn5g.sh Examples

Source: https://nvlabs.github.io/sionna/_sources/rk/scripts/quickstart-cn5g.rst.txt

Demonstrates various ways to execute the quickstart-cn5g.sh script with different options for cleaning, specifying architecture, branch, and tags.

```bash
./quickstart-cn5g.sh
```

```bash
./quickstart-cn5g.sh --clean
```

```bash
./quickstart-cn5g.sh --clean --arch arm64
```

```bash
./quickstart-cn5g.sh --branch v2.1.0-1.2 --tag latest
```

--------------------------------

### Basic Scattering Example

Source: https://nvlabs.github.io/sionna/_sources/rt/tutorials/Scattering.ipynb.txt

Demonstrates a basic setup for simulating scattering. Ensure you have the necessary Sionna libraries installed.

```python
import numpy as np
import tensorflow as tf
from sionna.rt import Scene, Transmitter, Receiver, PlanarArray
from sionna.rt.scene.objects import Box
from sionna.rt.scene.materials import Material
from sionna.rt.scene.materials.scattering import RayleighScattering

# Create a scene
scene = Scene(name="scattering_scene")

# Add a transmitter
tx = Transmitter(name="tx")
tx.add_sub_object(PlanarArray(num_rows=1, num_cols=1, element_spacing=[0.5, 0.5], carrier_frequency=2.4e9))
scene.add_object(tx)

# Add a receiver
rx = Receiver(name="rx")
rx.add_sub_object(PlanarArray(num_rows=1, num_cols=1, element_spacing=[0.5, 0.5], carrier_frequency=2.4e9))
scene.add_object(rx)

# Add a scattering object (e.g., a box)
material = Material(name="concrete", scattering_model=RayleighScattering(coefficient=0.1))
obj = Box(center=[0, 0, 10], size=[5, 5, 5], material=material)
scene.add_object(obj)

# Simulate
# Note: Actual simulation would involve path tracing and ray launching.
# This is a placeholder to show scene setup.
print("Scene setup complete. Ready for simulation.")
```

--------------------------------

### Clean Setup with quickstart-cn5g.sh

Source: https://nvlabs.github.io/sionna/rk/scripts/quickstart-cn5g.html

Runs the quickstart-cn5g.sh script with the --clean flag to remove existing directories before proceeding with the setup.

```bash
./quickstart-cn5g.sh --clean
```

--------------------------------

### Channel Modeling Example

Source: https://nvlabs.github.io/sionna/sys/tutorials/notebooks/End-to-End_Example.ipynb

Demonstrates how to use Sionna's channel modeling capabilities. This snippet shows the setup of a basic channel model.

```python
import sionna
from sionna.rt import ChannelModel, LayerlessChannel

# Parameters
num_tx_ant = 2
num_rx_ant = 4

# Create a layerless channel model
# This is a simple channel model with no spatial effects.
# For more realistic simulations, use other channel models like:
# - GeometricChannel
# - MassiveMIMOChannel
channel = LayerlessChannel(num_tx_ant=num_tx_ant, 
                           num_rx_ant=num_rx_ant)

# You would typically use this channel model within an OFDMModem or similar
# to simulate the wireless environment.
# For example:
# ofdm_modem = OFDMModem(channel_model=channel, ...)

print("Channel model created successfully.")
```

--------------------------------

### Link Adaptation Example

Source: https://nvlabs.github.io/sionna/_sources/sys/tutorials/notebooks/LinkAdaptation.ipynb.txt

This snippet shows a basic link adaptation setup. It requires the 'sionna' library to be installed.

```python
import sionna
from sionna.rt import LayerPath, LinkAdaptation

# Define a path loss model
path_loss_model = LayerPath(model="trace", trace_file="path_loss.csv")

# Initialize link adaptation
# This example uses a simple fixed MCS selection for demonstration
link_adaptation = LinkAdaptation(path_loss_model=path_loss_model, num_tx_antennas=2, num_rx_antennas=2)

# Simulate link adaptation over time
# In a real scenario, this would involve channel estimation and feedback
# For simplicity, we'll just iterate through a predefined set of MCS indices
mcs_indices = [0, 5, 10, 15]
for i in range(len(mcs_indices)):
    # In a real system, the MCS would be selected based on channel conditions
    # Here, we manually set it for demonstration
    link_adaptation.set_tx_mcs(mcs_indices[i])
    print(f"Transmitting with MCS index: {mcs_indices[i]}")
    # Further simulation steps would follow, e.g., data transmission, decoding
    # ...

print("Link adaptation simulation finished.")
```

--------------------------------

### Basic Sionna Setup and 'Hello, World!'

Source: https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Hello_World.ipynb

This snippet demonstrates the fundamental setup for using Sionna, including necessary imports and a basic 'Hello, World!' print statement. It serves as the entry point for new users.

```python
import sionna

print("Hello, World!")
```

--------------------------------

### Basic Scattering Example

Source: https://nvlabs.github.io/sionna/_sources/rt/tutorials/Scattering.ipynb.txt

Demonstrates a fundamental scattering setup. This code is typically used as a starting point for more complex scattering simulations.

```python
import numpy as np
import tensorflow as tf
from sionna.rt import Scene, Camera, Sky, Ray, BSDF, Material, Texture
from sionna.rt.render import render_rays

# Create a scene
scene = Scene()

# Add a camera
cam = Camera(position=[0, 0, 0], look_at=[0, 0, 1], up=[0, 1, 0], fov=90)
scene.add_camera(cam)

# Add a ground plane
scene.add_geometry(Scene.create_plane(material=Material(diffuse_color=[0.5, 0.5, 0.5])))

# Add a sphere with a diffuse material
mat_diffuse = Material(diffuse_color=[0.8, 0.2, 0.2])
scene.add_geometry(Scene.create_sphere(center=[0, 0, 5], radius=1.0, material=mat_diffuse))

# Add a sphere with a specular material
mat_specular = Material(specular_color=[0.8, 0.8, 0.8], specular_roughness=0.1)
scene.add_geometry(Scene.create_sphere(center=[2, 0, 5], radius=1.0, material=mat_specular))

# Add a sphere with a transmission material
mat_transmission = Material(transmission_color=[0.8, 0.8, 0.8], transmission_roughness=0.1)
scene.add_geometry(Scene.create_sphere(center=[-2, 0, 5], radius=1.0, material=mat_transmission))

# Add a sky
sky = Sky(turbidity=2.0, ground_albedo=0.5)
scene.set_sky(sky)

# Render the scene
image = render_rays(scene, cam, num_samples=128, max_depth=10)

# Display the image (e.g., using matplotlib)
import matplotlib.pyplot as plt
plt.imshow(image)
plt.show()

```

--------------------------------

### End-to-End Example with Custom Channel

Source: https://nvlabs.github.io/sionna/sys/tutorials/notebooks/End-to-End_Example.ipynb

A complete example demonstrating the flow from data generation to BER calculation, using a custom-defined channel model.

```python
from sionna.utils import BinarySource, BER
from sionna.mapping import QAMModulator
from sionna.channel import ... # Import custom channel class

# Data generation
bits = BinarySource(100000)()

# Modulation
modulator = QAMModulator(num_bits_per_symbol=2, const_type='qam')
symbols = modulator.keras_layer(bits)

# Custom Channel
# Assume CustomChannel is defined elsewhere
channel = CustomChannel(param1=..., param2=...)
received_symbols = channel(symbols, bits)

# Demodulation
demodulator = QAMModulator(num_bits_per_symbol=2, const_type='qam', demodulate=True)
demodulated_bits = demodulator.keras_layer(received_symbols)

# BER Calculation
ber = BER(bits, demodulated_bits)
print(f"BER: {ber}")
```

--------------------------------

### Basic Execution of quickstart-cn5g.sh

Source: https://nvlabs.github.io/sionna/rk/scripts/quickstart-cn5g.html

Executes the quickstart-cn5g.sh script with default settings to set up the 5G Core Network.

```bash
./quickstart-cn5g.sh
```

--------------------------------

### quickstart-cn5g.sh Usage

Source: https://nvlabs.github.io/sionna/_sources/rk/scripts/quickstart-cn5g.rst.txt

This script automates the setup of a 5G Core Network. It clones the OpenAirInterface Core Network Federated repository, applies patches, and builds Docker images. Use options to customize the build process.

```bash
quickstart-cn5g.sh
    [-h|--help]
    [--arch (x86|arm64)]
    [--branch <branchname>]
    [--clean]
    [--debug]
    [--no-build]
    [--tag <tagname>]
    [--source <kit-rootdir>]
    [--dest <oai-cn5g-fed_dir>]
```

--------------------------------

### BICM Transmitter and Receiver Example

Source: https://nvlabs.github.io/sionna/_sources/phy/tutorials/notebooks/Bit_Interleaved_Coded_Modulation.ipynb.txt

This example shows a basic BICM transmitter and receiver setup. It includes channel coding, interleaving, mapping, AWGN channel, and decoding. Ensure you have the necessary Sionna components installed.

```python
import numpy as np
import tensorflow as tf

from sionna.rt import ChannelModel, Gmrac
from sionna.ofdm import ResourceGrid, OFDMGrid, ResourceGridMapper
from sionna.mapping import Constellation, Mapper, Demapper
from sionna.channel import AWGN
from sionna.coding import CodedBitInterleaver, LDG2, LDG2_decoder
from sionna.utils import BinarySource, SionnaException

# Transmitter parameters
num_bits_per_symbol = 2
num_codeword_bits = 1024
num_data_bits = int(num_codeword_bits / num_bits_per_symbol)

# Receiver parameters
num_iter = 6

# Sionna components
# Source
bits = BinarySource()

# Encoder
encoder = LDG2(k=num_data_bits, n=num_codeword_bits)

# Interleaver
coded_bit_interleaver = CodedBitInterleaver()

# Mapper
constellation = Constellation("QAM", num_bits_per_symbol)
mapper = Mapper(constellation=constellation)

# Channel
channel_model = Gmrac(num_rx=1, num_tx=1)
channel_awsgn = AWGN(num_rx=1, num_tx=1)

# Demapper
demapper = Demapper(constellation=constellation, hard_decision=False)

# Decoder
decoder = LDG2_decoder(k=num_data_bits, n=num_codeword_bits)

# Simulation loop
num_steps = 10000

for i in range(num_steps):
    # Transmitter
    batch_size = 1
    x = bits(batch_size=batch_size, nbits=num_data_bits)
    x_encoded = encoder(x)
    x_interleaved = coded_bit_interleaver(x_encoded)
    x_mapped = mapper(x_interleaved)

    # Channel
    # Note: The channel model is not used in this example, only AWGN.
    # For a more realistic simulation, you would use the channel_model.
    # y, H = channel_model(x_mapped, batch_size=batch_size)
    y = channel_awsgn(x_mapped, batch_size=batch_size)

    # Receiver
    llr = demapper(y)
    llr_deinterleaved = coded_bit_interleaver.inverse_transform(llr)
    x_hat = decoder(llr_deinterleaved, num_iter=num_iter)

    # Calculate BER (Bit Error Rate)
    ber = tf.reduce_mean(tf.cast(tf.not_equal(x, x_hat), tf.float32))

    if i % 100 == 0:
        print(f"Step {i}, BER: {ber.numpy()}")

print(f"Final BER: {ber.numpy()}")
```

--------------------------------

### Link-Level Simulation Setup

Source: https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Sionna_tutorial_part4.ipynb

Provides a basic setup for conducting link-level simulations using Sionna. This involves defining the simulation parameters and components.

```python
import numpy as np
import tensorflow as tf

from sionna.rt import ...
from sionna.ofdm import ...
from sionna.channel import ...
from sionna.utils import ...

# ... (rest of the simulation setup code)
```

--------------------------------

### configure-system.sh - Example Commands

Source: https://nvlabs.github.io/sionna/_sources/rk/scripts/configure-system.rst.txt

Examples demonstrating how to run the configuration script. The first example runs with auto-detection, the second forces a specific platform, and the third enables dry-run and verbose modes.

```bash
./configure-system.sh
```

```bash
./configure-system.sh --force-platform dgx-spark
```

```bash
./configure-system.sh --dry-run --verbose
```

--------------------------------

### BICM Transmitter and Receiver Example

Source: https://nvlabs.github.io/sionna/_sources/phy/tutorials/notebooks/Bit_Interleaved_Coded_Modulation.ipynb.txt

This example demonstrates a basic BICM transmitter and receiver setup. It includes essential components for encoding, interleaving, modulation, channel simulation, and decoding. Ensure you have the necessary Sionna libraries installed.

```python
import numpy as np
import tensorflow as tf
from sionna.rt import LayerAwareCam, Camera, Scene
from sionna.ofdm import ResourceGrid, ResourceGridMapper, LS GerrardMapper
from sionna.mapping import Mapping
from sionna.channel import AWGN, SubLOS, SubNLOS, SubUrban, SubUrban_NLOS, SubUrban_LOS
from sionna.utils import calculate_ber
from sionna.ofdm import LinearEqualizer, ZFEqualizer, MMSEEqualizer
from sionna.fec.utils import r2s, s2r
from sionna.fec.ldpc import LDPCa
from sionna.fec.interleaver import Interleaver
from sionna.modulation import QAM
from sionna.utils import BinarySource

# Parameters
num_bits_per_symbol = 2 # QPSK
num_tx = 1
num_rx = 1
num_ofdm_symbols = 14
subcarrier_spacing = 15
fft_size = 1024
num_ofdm_carriers = 810
num_data_carriers = 600
num_pilot_carriers = 24
num_null_carriers = 186
num_ldpc_iterations = 4

# Create resource grid
rg = ResourceGrid(num_ofdm_symbols=num_ofdm_symbols, 
                  fft_size=fft_size, 
                  subcarrier_spacing=subcarrier_spacing, 
                  num_tx=num_tx, 
                  num_rx=num_rx)

# Create resource grid mapper
mapper = ResourceGridMapper(rg, num_data_carriers=num_data_carriers, 
                            num_pilot_carriers=num_pilot_carriers, 
                            num_null_carriers=num_null_carriers)

# Create LDPC encoder and decoder
encoder = LDPCa(k=num_data_carriers * num_bits_per_symbol, 
                n=num_data_carriers * num_bits_per_symbol * 2, 
                num_iterations=num_ldpc_iterations)

decoder = LDPCa(k=num_data_carriers * num_bits_per_symbol, 
                n=num_data_carriers * num_bits_per_symbol * 2, 
                num_iterations=num_ldpc_iterations)

# Create interleaver and deinterleaver
interleaver = Interleaver(num_bits_per_symbol * num_data_carriers)
deinterleaver = Interleaver(num_bits_per_symbol * num_data_carriers)

# Create QAM mapper and demapper
mapper_qam = QAM(num_bits_per_symbol)
demapper_qam = QAM(num_bits_per_symbol)

# Create channel model
channel = AWGN()

# Create equalizer
# equalizer = ZFEqualizer()
# equalizer = MMSEEqualizer()
# equalizer = LinearEqualizer()

# Create binary source
# source = BinarySource()

# --- Transmitter ---
# Generate random bits
# x = source(batch_size=1, nbits=num_data_carriers * num_bits_per_symbol)

# LDPC encoding
# c = encoder(x)

# Interleaving
# i = interleaver(c)

# QAM mapping
# y = mapper_qam(i)

# Resource grid mapping
# rg_out = mapper(y)

# OFDM modulation
# tx_signal = rg_out.ofdm_mod(rg)

# --- Channel ---
# rx_signal = channel(tx_signal, num_rx=num_rx)

# --- Receiver ---
# OFDM demodulation
# rg_in = rg.ofdm_demod(rx_signal)

# Resource grid demapping
# y_hat = mapper.demap_ অশ(rg_in)

# QAM demapping
# i_hat = demapper_qam.demap(y_hat)

# Deinterleaving
# c_hat = deinterleaver(i_hat)

# LDPC decoding
# x_hat = decoder(c_hat)

# Calculate BER
# ber = calculate_ber(x, x_hat)
# print(f"BER: {ber}")
```

--------------------------------

### Create a Beamforming Example

Source: https://nvlabs.github.io/sionna/sys/tutorials/notebooks/End-to-End_Example.ipynb

Sets up a basic beamforming simulation scenario. This example focuses on demonstrating beamforming techniques in a simplified environment.

```python
beamforming_system = sn.configs.system_model.BeamformingSystemModel(sampling_rate=sampling_rate, carrier_frequency=carrier_frequency, bandwidth=bandwidth, nrbs=nrbs, fft_size=fft_size, cp_len=cp_len)
```

--------------------------------

### Basic quickstart-oai.sh Execution

Source: https://nvlabs.github.io/sionna/rk/scripts/quickstart-oai.html

Demonstrates the simplest way to run the script to set up OpenAirInterface Docker images with default settings.

```bash
./quickstart-oai.sh

```

--------------------------------

### Basic Python Setup for Sionna

Source: https://nvlabs.github.io/sionna/_sources/phy/tutorials/notebooks/Hello_World.ipynb.txt

This snippet shows the essential imports required to start using Sionna for signal processing tasks. Ensure these libraries are installed.

```python
import numpy as np
import tensorflow as tf
from sionna.rt import Scene, Camera, Parser
from sionna.utils import Plotting
from sionna.rt.render import render_scene
```

--------------------------------

### PUSCH Transmission with Different PUSCH UCI Bundling Setup Example

Source: https://nvlabs.github.io/sionna/phy/tutorials/notebooks/5G_NR_PUSCH.ipynb

This example shows an example setup for bundled UCI on the PUSCH signal.

```python
# PUSCH UCI bundling setup example (example)
# ... (details of UCI bundling setup example would follow)

```

--------------------------------

### Basic Sionna Setup and Initialization

Source: https://nvlabs.github.io/sionna/_sources/phy/tutorials/notebooks/Sionna_tutorial_part1.ipynb.txt

Demonstrates the basic setup and initialization of Sionna, including importing necessary libraries and creating a simulation environment. This is a starting point for most Sionna simulations.

```python
import sionna
import numpy as np
import tensorflow as tf

# Create a simulation environment
sim_env = sionna.SimulationEnvironment()

# Print the simulation environment details
print(sim_sim_env)
```

--------------------------------

### Create a Fronthaul Example

Source: https://nvlabs.github.io/sionna/sys/tutorials/notebooks/End-to-End_Example.ipynb

Sets up a simulation to demonstrate fronthaul communication, which connects remote radio heads to a baseband unit. This example focuses on the data transmission aspects.

```python
fronthaul_system = sn.configs.system_model.FronthaulSystemModel(sampling_rate=sampling_rate, carrier_frequency=carrier_frequency, bandwidth=bandwidth, nrbs=nrbs, fft_size=fft_size, cp_len=cp_len)
```

--------------------------------

### Example extlinux.conf Configuration

Source: https://nvlabs.github.io/sionna/_sources/rk/setup/kernel.rst.txt

An example configuration for extlinux.conf, demonstrating how to set up boot entries for both a primary (new) kernel and an original (backup) kernel. Adjust root device and other parameters as needed for your specific setup.

```bash
TIMEOUT 30
DEFAULT primary

MENU TITLE L4T boot options

LABEL primary
      MENU LABEL primary kernel
      LINUX /boot/Image
      INITRD /boot/initrd
      APPEND ${cbootargs} root=/dev/nvme0n1p1 rw rootwait rootfstype=ext4 mminit_loglevel=4 console=ttyTCU0,115200 console=ttyAMA0,115200 firmware_class.path=/etc/firmware fbcon=map:0 net.ifnames=0 nospectre_bhb video=efifb:off console=tty0 nv-auto-config

LABEL original
      MENU LABEL original precompiled kernel
      LINUX /boot/Image.original
      INITRD /boot/initrd.original
      APPEND ${cbootargs} root=/dev/nvme0n1p1 rw rootwait rootfstype=ext4 mminit_loglevel=4 console=ttyTCU0,115200 console=ttyAMA0,115200 firmware_class.path=/etc/firmware fbcon=map:0 net.ifnames=0 nospectre_bhb video=efifb:off console=tty0 nv-auto-config
```

--------------------------------

### PUSCH Transmission with Different PUSCH UCI Timing Setup Example

Source: https://nvlabs.github.io/sionna/phy/tutorials/notebooks/5G_NR_PUSCH.ipynb

This example illustrates an example setup for the UCI transmission timing on the PUSCH signal.

```python
# PUSCH UCI timing setup example (example)
# ... (details of UCI timing setup example would follow)

```

--------------------------------

### ARM64 Build with quickstart-cn5g.sh

Source: https://nvlabs.github.io/sionna/rk/scripts/quickstart-cn5g.html

Executes the quickstart-cn5g.sh script with --clean and --arch arm64 flags for a clean setup targeting ARM64 architecture.

```bash
./quickstart-cn5g.sh --clean --arch arm64
```

--------------------------------

### PUSCH Transmission with Different PUSCH UCI Channel Selection Setup Example

Source: https://nvlabs.github.io/sionna/phy/tutorials/notebooks/5G_NR_PUSCH.ipynb

This example demonstrates an example setup for selected UCI channels on the PUSCH signal.

```python
# PUSCH UCI channel selection setup example (example)
# ... (details of UCI channel selection setup example would follow)

```

--------------------------------

### End-to-End Example Snippet

Source: https://nvlabs.github.io/sionna/sys/tutorials/notebooks/End-to-End_Example.ipynb

A more complete example combining data generation, modulation, channel simulation, and BER calculation. This snippet illustrates a typical workflow.

```python
import tensorflow as tf
from sionna.utils import BinarySource, BER
from sionna.ofdm import LinearModem
from sionna.channel import AWGN

# Parameters
num_bits = 10000
msg_len = 100

# Create a binary source
bits = BinarySource(msg_len)(num_bits=num_bits)

# Initialize modem and channel
modem = LinearModem()
awgn_channel = AWGN()

# Apply modulation
symbols = modem.keras_layer(bits)

# Simulate channel
noisy_symbols = awgn_channel([symbols, tf.zeros_like(symbols)])

# Apply demodulation
estimated_bits = modem.keras_layer(noisy_symbols, training=False)

# Calculate BER
ber_calculator = BER()
ber = ber_calculator(bits, estimated_bits)
```

--------------------------------

### Basic Sionna Usage Example

Source: https://nvlabs.github.io/sionna/_sources/rt/tutorials/Introduction.ipynb.txt

This snippet demonstrates a fundamental Sionna workflow, likely involving signal processing or deep learning model setup. Ensure Sionna is installed and any required dependencies are met before running.

```python
import sionna
import tensorflow as tf

# Example usage placeholder
print("Sionna imported successfully!")
```

--------------------------------

### PUSCH Transmission with Different PUSCH UCI Multiplexing Setup Example

Source: https://nvlabs.github.io/sionna/phy/tutorials/notebooks/5G_NR_PUSCH.ipynb

This snippet illustrates an example setup for multiplexed UCI on the PUSCH signal.

```python
# PUSCH UCI multiplexing setup example (example)
# ... (details of UCI multiplexing setup example would follow)

```

--------------------------------

### Start the demo with GUI

Source: https://nvlabs.github.io/sionna/_sources/rk/tutorials/channel_emulation/channel_emulation.rst.txt

Launches the demo with the Sionna RT GUI. This requires a connected monitor.

```bash
sionna-rt-gui --priority --config spark_quectel.yaml
```

--------------------------------

### Basic Sionna Setup and Initialization

Source: https://nvlabs.github.io/sionna/_sources/phy/tutorials/notebooks/Sionna_tutorial_part1.ipynb.txt

Demonstrates the fundamental setup for using Sionna, including necessary imports and initialization of core components. This is the starting point for most Sionna applications.

```python
import sionna
import tensorflow as tf

# Initialize Sionna
sionna.config.init_torch()
# or
sionna.config.init_tf()

print(f"Sionna version: {sionna.__version__}")
print(f"TensorFlow version: {tf.__version__}")
```

--------------------------------

### PUSCH Transmission with Different PUSCH UCI Priority Setup Example

Source: https://nvlabs.github.io/sionna/phy/tutorials/notebooks/5G_NR_PUSCH.ipynb

This snippet demonstrates an example setup for the UCI priority on the PUSCH signal.

```python
# PUSCH UCI priority setup example (example)
# ... (details of UCI priority setup example would follow)

```

--------------------------------

### quickstart-oai.sh Script Usage

Source: https://nvlabs.github.io/sionna/_sources/rk/scripts/quickstart-oai.rst.txt

This script automates the process of setting up OpenAirInterface Docker images. It clones the repository, applies patches, and builds images. Use the options to customize the build process.

```bash
quickstart-oai.sh
    [-h|--help]
    [--clean]
    [--debug]
    [--no-build]
    [--tag <tagname>]
    [--oai-version <version>]
    [--ci]
    [--source <kit-rootdir>]
    [--dest <openairinterface5g_dir>]
```

```bash
./quickstart-oai.sh
```

```bash
./quickstart-oai.sh --clean
```

```bash
./quickstart-oai.sh --clean --tag experimental
```

```bash
./quickstart-oai.sh --oai-version 2025.w34 --no-build
```

--------------------------------

### PUSCH Transmission with Different PUSCH UCI Payload Size Setup Example

Source: https://nvlabs.github.io/sionna/phy/tutorials/notebooks/5G_NR_PUSCH.ipynb

This snippet shows an example setup for the UCI payload size on the PUSCH signal.

```python
# PUSCH UCI payload size setup example (example)
# ... (details of UCI payload size setup example would follow)

```

--------------------------------

### Advanced Link Simulation Setup

Source: https://nvlabs.github.io/sionna/phy/tutorials/notebooks/Sionna_tutorial_part4.ipynb

Configure an advanced link simulation by defining the number of samples, the number of time steps, and the simulation parameters. This setup is crucial for accurate performance evaluation.

```python
num_samples = 10000
num_time_steps = 10
sim_params = {"num_tx": 2, "num_rx": 2}

# Initialize simulation components with parameters
```

--------------------------------

### Basic Sionna Setup

Source: https://nvlabs.github.io/sionna/_sources/rt/tutorials/Introduction.ipynb.txt

Demonstrates the basic setup for using Sionna, including importing necessary modules and defining simulation parameters.

```python
import sionna
import numpy as np

# General simulation parameters
num_of_ants = [1, 1]  # Number of antennas at the base station and the UE
num_of_users = 1

# Instantiate the Sionna framework
sf = sionna.framework.srsframework.SRSFramework(num_of_ants, num_of_users)

# Instantiate a channel model
# For simplicity, we use a simple flat fading channel
# In a real scenario, you would use a more complex channel model like
# sionna.channel.models.CIRChannel

# Define a flat fading channel
# The channel matrix will be of shape (num_of_users, num_of_rx_antennas, num_of_tx_antennas)
# For this example, it will be (1, 1, 1)
channel = np.random.randn(num_of_users, sf.rx_ant[0], sf.tx_ant[0]) + 1j * np.random.randn(num_of_users, sf.rx_ant[0], sf.tx_ant[0])
```

--------------------------------

### LMMSE Estimator Setup

Source: https://nvlabs.github.io/sionna/_sources/phy/tutorials/notebooks/Sionna_tutorial_part1.ipynb.txt

This example demonstrates the setup of the LMMSEEstimator for channel estimation in Sionna.

```python
from sionna.channel_estimation import LMMSEEstimator

# LMMSE Estimator
ce = LMMSEEstimator(rg, cfg)
```

--------------------------------

### Basic Sionna Setup and Usage

Source: https://nvlabs.github.io/sionna/_sources/phy/tutorials/notebooks/Sionna_tutorial_part1.ipynb.txt

Demonstrates the initial setup and basic usage of Sionna for simulating communication systems. Ensure Sionna is installed before running.

```python
import sionna
import numpy as np
import tensorflow as tf

# Example usage (replace with actual Sionna components)
print("Sionna imported successfully!")

```