kaira.models.fec.encoders.BaseBlockCodeEncoder

Inheritance diagram for BaseBlockCodeEncoder

class kaira.models.fec.encoders.BaseBlockCodeEncoder(code_length: int, code_dimension: int, *args: Any, **kwargs: Any)

Bases: BaseModel, ABC

Base class for block code encoders.

This abstract class provides a common interface and functionality for all types of block code encoders. It serves as a foundation for specific implementations like linear block codes, cyclic codes, BCH codes, etc.

Block codes transform k information bits into n coded bits (n > k), providing error detection and correction capabilities. The redundancy added during encoding enables the receiver to detect and possibly correct errors introduced by the channel.

Parameters:

• code_length (int) – The length of the codeword (n)

• code_dimension (int) – The dimension of the code (k)

• *args – Variable positional arguments passed to the base class

• **kwargs – Variable keyword arguments passed to the base class

Raises:

ValueError – If code parameters are invalid (e.g., non-positive or dimension > length)

Note

All concrete implementations must override the forward method to provide specific encoding behavior. The inverse_encode and calculate_syndrome methods are available in LinearBlockCodeEncoder for codes that support these operations.

Methods

__init__	Initialize the block code encoder with specified parameters.
extract_message	Extract the message bits from a codeword.
forward	Apply the encoding operation to the input tensor.

Attributes

code_dimension	Get the code dimension (k).
code_length	Get the codeword length (n).
code_rate	Get the rate of the code (k/n).
parity_bits	Get the number of parity bits (synonym for redundancy).
redundancy	Get the code redundancy (r = n - k).

__init__(code_length: int, code_dimension: int, *args: Any, **kwargs: Any)

Initialize the block code encoder with specified parameters.

Sets up the basic code parameters and validates that they meet the requirements for a valid block code (positive length, positive dimension, dimension <= length).

property code_length: int

Get the codeword length (n).

Returns:

The number of bits in each codeword after encoding

property code_dimension: int

Get the code dimension (k).

Returns:

The number of information bits encoded in each codeword

property redundancy: int

Get the code redundancy (r = n - k).

Returns:

The number of redundant bits added during encoding

property parity_bits: int

Get the number of parity bits (synonym for redundancy).

Returns:

The number of parity/check bits in each codeword

property code_rate: float

Get the rate of the code (k/n).

The code rate is a measure of efficiency, representing the proportion of the total bits that carry information (as opposed to redundancy).

Returns:

The ratio of information bits to total bits (between 0 and 1)

extract_message(codeword: Tensor) → Tensor

Extract the message bits from a codeword.

By default, this calls inverse_encode and returns just the decoded message. Subclasses can override this method to provide more efficient implementations.

Parameters:

codeword – Codeword tensor with shape (…, n) where n is the code length

Returns:

Extracted message tensor with shape (…, k) where k is the code dimension

Note

This implementation assumes the inverse_encode method can handle a single codeword correctly. Specific code types may override this with more efficient implementations.

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

Apply the encoding operation to the input tensor.

Transforms message bits into codewords by adding redundancy according to the specific encoding scheme implemented by the subclass.

Parameters:

• x – Input tensor containing message bits. The last dimension should be a multiple of the code dimension (k).

• *args – Additional positional arguments for specific encoder implementations.

• **kwargs – Additional keyword arguments for specific encoder implementations.

Returns:

Encoded tensor with codewords. Has the same shape as the input except the last dimension is expanded by a factor of n/k.

Raises:

ValueError – If the last dimension of x is not a multiple of k.

Note

The specific encoding method depends on the subclass implementation. For example, linear codes use matrix multiplication, while other codes may use different algorithms.