PAM2QAM#

class sionna.phy.mapping.PAM2QAM(num_bits_per_symbol: int, hard_in_out: bool = True, precision: Literal['single', 'double'] | None = None, device: str | None = None, **kwargs: Any)[source]#

Bases: sionna.phy.object.Object

Transforms PAM symbol indices/logits to QAM symbol indices/logits.

For two PAM constellation symbol indices or logits, corresponding to the real and imaginary components of a QAM constellation, compute the QAM symbol index or logits.

Parameters:

  • num_bits_per_symbol (int) – Number of bits per QAM constellation symbol, e.g., 4 for QAM16.

  • hard_in_out (bool) – Determines if inputs and outputs are indices or logits over constellation symbols. Defaults to True.

  • 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:

  • pam1 – torch.Tensor, torch.int32, or […, 2**(num_bits_per_symbol/2)], torch.float. Indices or logits for the first PAM constellation.

  • pam2 – torch.Tensor, torch.int32, or […, 2**(num_bits_per_symbol/2)], torch.float. Indices or logits for the second PAM constellation.

Outputs:

qam – torch.Tensor, torch.int32, or […, 2**num_bits_per_symbol], torch.float. Indices or logits for the corresponding QAM constellation.

Examples

import torch
from sionna.phy.mapping import PAM2QAM

converter = PAM2QAM(4)  # 16-QAM
ind_pam1 = torch.tensor([0, 1, 2, 3])
ind_pam2 = torch.tensor([0, 1, 2, 3])
ind_qam = converter(ind_pam1, ind_pam2)
print(ind_qam)
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