kaira.channels.FlatFadingChannel

Inheritance diagram of FlatFadingChannel — Inheritance diagram for FlatFadingChannel

class kaira.channels.FlatFadingChannel(fading_type: str, coherence_time: int, k_factor: float | None = None, avg_noise_power: float | None = None, snr_db: float | None = None, shadow_sigma_db: float | None = None, *args: Any, **kwargs: Any)[source]

Bases: BaseChannel

Flat fading channel with configurable distribution and coherence time.

Models a wireless channel where the fading coefficient remains constant over a specified coherence time and then changes to a new independent realization. This represents blockwise fading commonly used in communications analysis [Tse and Viswanath, 2005] [Rappaport, 2024].

Mathematical Model:: y[i] = h[⌊i/L⌋] * x[i] + n[i] where L is the coherence length, h follows a specified distribution, and n ~ CN(0,σ²)

Parameters:

fading_type (str) – Distribution type for fading coefficients (‘rayleigh’, ‘rician’, or ‘lognormal’)
coherence_time (int) – Number of samples over which the fading coefficient remains constant
k_factor (float, optional) – Rician K-factor (ratio of direct to scattered power), used only when fading_type=’rician’
avg_noise_power (float, optional) – The average noise power σ²
snr_db (float, optional) – SNR in dB (alternative to avg_noise_power)
shadow_sigma_db (float, optional) – Standard deviation in dB for log-normal shadowing, used only when fading_type=’lognormal’

Example

>>> # Create a flat Rayleigh fading channel with coherence time of 10 samples
>>> channel = FlatFadingChannel('rayleigh', coherence_time=10, snr_db=15)
>>> x = torch.complex(torch.ones(100), torch.zeros(100))
>>> y = channel(x)  # Output with block fading effects

Methods

`__init__`	Initialize the Flat Fading channel.
`forward`	Apply flat fading and noise to the input signal.
`get_config`	Get a dictionary of the channel's configuration.

Attributes

`k_factor`
`avg_noise_power`
`snr_db`
`shadow_sigma_db`

Examples using `kaira.channels.FlatFadingChannel`

Channel Comparison

Composing Multiple Channel Effects

Fading Channels in Wireless Communications

Rician Fading vs Rayleigh Fading Channels

Differential Phase-Shift Keying (DPSK)

π/4-QPSK Modulation

Performance Metrics Visualization

Original DeepJSCC Model (Bourtsoulatze 2019)

Channel Capacity Analysis with Kaira

__init__(fading_type: str, coherence_time: int, k_factor: float | None = None, avg_noise_power: float | None = None, snr_db: float | None = None, shadow_sigma_db: float | None = None, *args: Any, **kwargs: Any)[source]

Initialize the Flat Fading channel.

Parameters:

fading_type (str) – Distribution type (‘rayleigh’, ‘rician’, ‘lognormal’).
coherence_time (int) – Samples over which fading is constant.
k_factor (float, optional) – Rician K-factor (for ‘rician’).
avg_noise_power (float, optional) – Average noise power σ².
snr_db (float, optional) – SNR in dB (alternative to avg_noise_power).
shadow_sigma_db (float, optional) – Shadowing std dev in dB (for ‘lognormal’).
*args – Variable length argument list passed to the base class.
**kwargs – Arbitrary keyword arguments passed to the base class.

k_factor: float | None

shadow_sigma_db: float | None

get_config() → Dict[str, Any]

Get a dictionary of the channel’s configuration.

This method returns a dictionary containing the channel’s parameters, which can be used to recreate the channel instance.

Returns:: Dictionary of parameter names and values
Return type:: Dict[str, Any]

avg_noise_power: float | None

snr_db: float | None

forward(x: Tensor, *args: Any, csi=None, noise=None, **kwargs: Any) → Tensor[source]

Apply flat fading and noise to the input signal.

Parameters:

x (torch.Tensor) – The input tensor.
*args – Additional positional arguments (unused).
csi (Optional[torch.Tensor]) – Pre-computed channel state information (fading coefficients). If provided, these coefficients are used instead of generating new ones.
noise (Optional[torch.Tensor]) – Pre-generated noise tensor. If provided, this noise is added.
**kwargs – Additional keyword arguments (unused).

Returns:

The output tensor after applying fading and noise.

Return type:

torch.Tensor

kaira.channels.FlatFadingChannel

Examples using kaira.channels.FlatFadingChannel

Examples using `kaira.channels.FlatFadingChannel`