PolarSCDecoder#

class sionna.phy.fec.polar.PolarSCDecoder(frozen_pos: numpy.ndarray, n: int, *, precision: str | None = None, device: str | None = None, **kwargs)[source]#

Bases: sionna.phy.block.Block

Successive cancellation (SC) decoder [Arikan_Polar] for Polar codes and Polar-like codes.

Parameters:

frozen_pos (numpy.ndarray) – Array of int defining the n-k indices of the frozen positions.
n (int) – Defining the codeword length.
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 channel LLR values (as logits).

Outputs:

u_hat – […, k], torch.float. Tensor containing hard-decided estimations of all k information bits.

Notes

This block implements the SC decoder as described in [Arikan_Polar]. However, the implementation follows the recursive tree [Gross_Fast_SCL] terminology and combines nodes for increased throughputs without changing the outcome of the algorithm.

As commonly done, we assume frozen bits are set to 0. Please note that - although its practical relevance is only little - setting frozen bits to 1 may result in affine codes instead of linear code as the all-zero codeword is not necessarily part of the code any more.

Examples

import torch
from sionna.phy.fec.polar import PolarSCDecoder, PolarEncoder
from sionna.phy.fec.polar.utils import generate_5g_ranking

k, n = 100, 256
frozen_pos, _ = generate_5g_ranking(k, n)
encoder = PolarEncoder(frozen_pos, n)
decoder = PolarSCDecoder(frozen_pos, n)

bits = torch.randint(0, 2, (10, k), dtype=torch.float32)
codewords = encoder(bits)
llr_ch = 20.0 * (2.0 * codewords - 1)  # BPSK without noise
decoded = decoder(llr_ch)
print(torch.equal(bits, decoded))
# True

Attributes

property n: int#: Codeword length.

property k: int#: Number of information bits.

property frozen_pos: numpy.ndarray#: Frozen positions for Polar decoding.

property info_pos: numpy.ndarray#: Information bit positions for Polar encoding.

property llr_max: float#: Maximum LLR value for internal calculations.

Methods

build(input_shape: Tuple[int, ...]) → None[source]#

Check if shape of input is invalid.

Parameters:: input_shape (Tuple[int, ...])

PolarSCDecoder#

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