SymbolLogits2Moments#

class sionna.phy.mapping.SymbolLogits2Moments(constellation_type: str | None = None, num_bits_per_symbol: int | None = None, constellation: sionna.phy.mapping.Constellation | None = None, precision: Literal['single', 'double'] | None = None, device: str | None = None, **kwargs: Any)[source]#

Bases: sionna.phy.block.Block

Computes the mean and variance of a constellation from logits (unnormalized log-probabilities) on the constellation points.

More precisely, given a constellation \(\mathcal{C} = \left[ c_0,\dots,c_{N-1} \right]\) of size \(N\), this layer computes the mean and variance according to

\[\begin{split}\begin{aligned} \mu &= \sum_{n = 0}^{N-1} c_n \Pr \left(c_n \lvert \mathbf{\ell} \right)\\ \nu &= \sum_{n = 0}^{N-1} \left( c_n - \mu \right)^2 \Pr \left(c_n \lvert \mathbf{\ell} \right) \end{aligned}\end{split}\]

where \(\mathbf{\ell} = \left[ \ell_0, \dots, \ell_{N-1} \right]\) are the logits, and

\[\Pr \left(c_n \lvert \mathbf{\ell} \right) = \frac{\exp \left( \ell_n \right)}{\sum_{i=0}^{N-1} \exp \left( \ell_i \right) }.\]
Parameters:

  • constellation_type (str | None) – Type of constellation. One of “qam”, “pam”, or “custom”. For “custom”, an instance of Constellation must be provided.

  • num_bits_per_symbol (int | None) – The number of bits per constellation symbol, e.g., 4 for QAM16. Only required for constellation_type in [“qam”, “pam”].

  • constellation (sionna.phy.mapping.Constellation | None) – If no constellation is provided, constellation_type and num_bits_per_symbol must be provided. Defaults to None.

  • precision (Literal['single', 'double'] | None) – Precision used for internal calculations and outputs. If set to None, precision is used.

  • device (str | None) – Device for tensor operations. If None, device is used.

  • kwargs (Any)

Inputs:

logits – […, n, num_points], torch.float. Logits on constellation points.

Outputs:

  • mean – […, n], torch.complex. Mean of the constellation.

  • var – […, n], torch.float. Variance of the constellation.

Examples

import torch
from sionna.phy.mapping import SymbolLogits2Moments

converter = SymbolLogits2Moments("qam", 4)
logits = torch.randn(10, 25, 16)  # 10 batches, 25 symbols, 16 constellation points
mean, var = converter(logits)
print(mean.shape, var.shape)
# torch.Size([10, 25]) torch.Size([10, 25])
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