kaira.channels.BinarySymmetricChannel

Inheritance diagram for BinarySymmetricChannel

class kaira.channels.BinarySymmetricChannel(crossover_prob, *args: Any, **kwargs: Any)

Bases: BaseChannel

Binary Symmetric Channel (BSC) with symmetric bit flip probability.

The Binary Symmetric Channel is a fundamental model in information theory where each bit is independently flipped with probability p. It represents a binary channel with symmetric crossover characteristics [Cover and Thomas, 2006].

Mathematical Model:

P(y=0|x=1) = P(y=1|x=0) = p (crossover probability) P(y=0|x=0) = P(y=1|x=1) = 1-p

Parameters:

crossover_prob (float) – Probability of bit flip (0 ≤ p ≤ 0.5)

Example

>>> channel = BinarySymmetricChannel(crossover_prob=0.1)
>>> x = torch.tensor([0, 1, 0, 1], dtype=torch.float32)
>>> y = channel(x)  # Some bits will be flipped with 10% probability

Methods

__init__	Initialize the Binary Symmetric Channel.
forward	Apply Binary Symmetric Channel errors to the input tensor.
get_config	Get a dictionary of the channel's configuration.

__init__(crossover_prob, *args: Any, **kwargs: Any)

Initialize the Binary Symmetric Channel.

Parameters:

• crossover_prob (float) – Probability of bit flip (0 ≤ p ≤ 0.5).

• *args – Variable length argument list passed to the base class.

• **kwargs – Arbitrary keyword arguments passed to the base class.

forward(x: Tensor, *args: Any, **kwargs: Any) → Tensor

Apply Binary Symmetric Channel errors to the input tensor.

Parameters:

• x (torch.Tensor) – The input tensor (binary 0/1 or bipolar -1/1).

• *args – Additional positional arguments (unused).

• **kwargs – Additional keyword arguments (unused).

Returns:

The output tensor with potential bit flips.

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]