BCJRDecoder#

class sionna.phy.fec.conv.BCJRDecoder(encoder: ConvEncoder | None = None, gen_poly: Tuple[str, ...] | None = None, rate: float = 0.5, constraint_length: int = 3, rsc: bool = False, terminate: bool = False, hard_out: bool = True, algorithm: str = 'map', precision: str | None = None, device: str | None = None, **kwargs)[source]#

Bases: sionna.phy.block.Block

Applies BCJR decoding to a sequence of noisy codeword bits.

Implements the BCJR decoding algorithm [BCJR] that returns an estimate of the information bits for a noisy convolutional codeword. Takes as input channel LLRs and optional a priori LLRs. Returns an estimate of the information bits, either output LLRs (hard_out = False) or hard decoded bits (hard_out = True), respectively.

Parameters:

  • encoder (ConvEncoder | None) – If encoder is provided as input, the following input parameters are not required and will be ignored: gen_poly, rate, constraint_length, rsc, terminate. They will be inferred from the encoder object itself. If None, the above parameters must be provided explicitly.

  • gen_poly (Tuple[str, ...] | None) – Tuple of strings with each string being a 0,1 sequence. If None, rate and constraint_length must be provided.

  • rate (float) – Valid values are 1/3 and 1/2. Only required if gen_poly is None.

  • constraint_length (int) – Valid values are between 3 and 8 inclusive. Only required if gen_poly is None.

  • rsc (bool) – Boolean flag indicating whether the encoder is recursive-systematic for given generator polynomials. True indicates encoder is recursive-systematic. False indicates encoder is feed-forward non-systematic. Defaults to False.

  • terminate (bool) – Boolean flag indicating whether the codeword is terminated. True indicates codeword is terminated to all-zero state. False indicates codeword is not terminated. Defaults to False.

  • hard_out (bool) – Boolean flag indicating whether to output hard or soft decisions on the decoded information vector. True implies a hard-decoded information vector of 0/1’s as output. False implies output is decoded LLRs of the information. Defaults to True.

  • algorithm (str) – Indicates the implemented BCJR algorithm, where 'map' denotes the exact MAP algorithm, 'log' indicates the exact MAP implementation but in log-domain, and 'maxlog' indicates the approximated MAP implementation in log-domain where \(\log(e^{a}+e^{b}) \sim \max(a,b)\).

  • precision (str | None) – Precision used for internal calculations and outputs. If None, precision is used.

  • device (str | None) – Device for computation (e.g., ‘cpu’, ‘cuda:0’). If None, device is used.

Inputs:

  • llr_ch – […, n], torch.float. Tensor containing the (noisy) channel LLRs, where n denotes the codeword length.

  • llr_a – […, k], None (default) | torch.float. Tensor containing the a priori information of each information bit. Implicitly assumed to be 0 if only llr_ch is provided.

Outputs:

msghat – torch.float. Tensor of shape [..., coderate*n] containing the estimates of the information bit tensor.

Examples

from sionna.phy.fec.conv import BCJRDecoder

decoder = BCJRDecoder(rate=0.5, constraint_length=5)
llr = torch.randn(10, 200)  # Received LLRs
u_hat = decoder(llr)
print(u_hat.shape)
# torch.Size([10, 100])

Attributes

property gen_poly: Tuple[str, ...]#

Generator polynomial used by the encoder.

property coderate: float#

Rate of the code used in the encoder.

property trellis: sionna.phy.fec.conv.utils.Trellis#

Trellis object used during encoding.

property terminate: bool#

Indicates if the encoder is terminated during codeword generation.

property k: int | None#

Number of information bits per codeword.

property n: int | None#

Number of codeword bits.

Methods

build(llr_ch_shape: torch.Size, **kwargs)[source]#

Build block and check dimensions.

Parameters:

llr_ch_shape (torch.Size)