kaira.channels.RicianFadingChannel

Inheritance diagram of RicianFadingChannel

Inheritance diagram for RicianFadingChannel

class kaira.channels.RicianFadingChannel(k_factor: float = 1.0, coherence_time=1, avg_noise_power: float | None = None, snr_db: float | None = None, *args: Any, **kwargs: Any)[source]

Bases: FlatFadingChannel

Rician fading channel with configurable K-factor and coherence time.

A specialized version of FlatFadingChannel that uses Rician fading. Suitable for modeling wireless channels with a dominant direct path plus multiple weaker reflection paths.

Mathematical Model:

y = h*x + n where h follows a Rician distribution with K-factor and n ~ CN(0,σ²)

The K-factor represents the ratio of power in the direct path to the power in the scattered paths. Higher K values indicate a stronger line-of-sight component.

Parameters:

  • k_factor (float) – Rician K-factor (ratio of direct to scattered power)

  • coherence_time (int) – Number of samples over which the fading coefficient remains constant

  • avg_noise_power (float, optional) – The average noise power

  • snr_db (float, optional) – SNR in dB (alternative to avg_noise_power)

Example

>>> # Create a Rician channel with K=5 (strong direct path)
>>> channel = RicianFadingChannel(k_factor=5, coherence_time=10, snr_db=15)
>>> x = torch.complex(torch.ones(100), torch.zeros(100))
>>> y = channel(x)  # Output with Rician fading

Methods

__init__

Initialize the Rician 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

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__(k_factor: float = 1.0, coherence_time=1, avg_noise_power: float | None = None, snr_db: float | None = None, *args: Any, **kwargs: Any)[source]

Initialize the Rician Fading channel.

Parameters:

  • k_factor (float) – Rician K-factor. Defaults to 1.0.

  • coherence_time (int) – Samples over which fading is constant. Defaults to 1.

  • avg_noise_power (float, optional) – Average noise power.

  • 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.