kaira.channels.RayleighFadingChannel

Inheritance diagram of RayleighFadingChannel — Inheritance diagram for RayleighFadingChannel

class kaira.channels.RayleighFadingChannel(coherence_time=1, avg_noise_power: float | None = None, snr_db: float | None = None, *args: Any, **kwargs: Any)[source]

Bases: FlatFadingChannel

Specialized channel for Rayleigh fading in wireless communications.

This is a convenience class that creates a FlatFadingChannel with the fading_type set to “rayleigh” to model Rayleigh fading, which is common in non-line-of-sight wireless propagation environments.

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

Parameters:

coherence_time (int, optional) – Number of samples over which the fading coefficient remains constant. Defaults to 1.
avg_noise_power (float, optional) – The average noise power σ²
snr_db (float, optional) – SNR in dB (alternative to avg_noise_power)

Example

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

Methods

`__init__`	Initialize the Rayleigh 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.RayleighFadingChannel`

Channel Comparison

Rician Fading vs Rayleigh Fading Channels

Performance Metrics Visualization

Channel Capacity Analysis with Kaira

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

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

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]

k_factor: float | None

avg_noise_power: float | None

snr_db: float | None

shadow_sigma_db: float | None

__init__(coherence_time=1, avg_noise_power: float | None = None, snr_db: float | None = None, *args: Any, **kwargs: Any)[source]

Initialize the Rayleigh Fading channel.

Parameters:

coherence_time (int, optional) – Samples over which fading is constant. Defaults to 1.
avg_noise_power (float, optional) – Average noise power σ².
snr_db (float, optional) – SNR in dB (alternative to avg_noise_power).
*args – Variable length argument list passed to the base class.
**kwargs – Arbitrary keyword arguments passed to the base class.

kaira.channels.RayleighFadingChannel

Examples using kaira.channels.RayleighFadingChannel

Examples using `kaira.channels.RayleighFadingChannel`