LMMSEPostEqualizationSINR#

class sionna.phy.ofdm.LMMSEPostEqualizationSINR(resource_grid: sionna.phy.ofdm.resource_grid.ResourceGrid, stream_management: sionna.phy.mimo.stream_management.StreamManagement, precision: Literal['single', 'double'] | None = None, device: str | None = None, **kwargs)[source]#

Bases: sionna.phy.ofdm.equalization.PostEqualizationSINR

Block that computes the SINR after LMMSE equalization.

The equalization matrix is the one computed by lmmse_matrix().

Parameters:

resource_grid (sionna.phy.ofdm.resource_grid.ResourceGrid) – ResourceGrid to be used
stream_management (sionna.phy.mimo.stream_management.StreamManagement) – StreamManagement to be used
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.

Inputs:

h_eff – [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], torch.complex. Effective channel after precoding as defined in (60).
no – [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, num_effective_subcarriers] (or only the first n dims), torch.float. Noise variance.
h_eff_hat – None (default) | [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], torch.complex. Estimated effective channel after precoding. If set to None, the actual channel realizations are used.
interference_whitening – bool (default=True). If set to True, also the interference from undesired streams (e.g., from other cells) is whitened.

Outputs:

sinr – [batch_size, num_ofdm_symbols, num_effective_subcarriers, num_rx, num_streams_per_rx], torch.float. SINR after equalization.

Examples

import numpy as np
import torch
from sionna.phy.ofdm import ResourceGrid, LMMSEPostEqualizationSINR
from sionna.phy.mimo import StreamManagement

rg = ResourceGrid(num_ofdm_symbols=14,
                  fft_size=64,
                  subcarrier_spacing=30e3,
                  num_tx=2,
                  num_streams_per_tx=2)
sm = StreamManagement(np.ones([1, 2]), 2)
sinr_computer = LMMSEPostEqualizationSINR(rg, sm)

batch_size = 16
h_eff = torch.randn(batch_size, 1, 4, 2, 2, 14, 64, dtype=torch.complex64)
no = torch.ones(1) * 0.1

sinr = sinr_computer(h_eff, no)
print(sinr.shape)
# torch.Size([16, 14, 64, 1, 4])

LMMSEPostEqualizationSINR#

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