BaseChannelEstimator

class sionna.phy.ofdm.BaseChannelEstimator(resource_grid: sionna.phy.ofdm.resource_grid.ResourceGrid, interpolation_type: str = 'nn', interpolator: sionna.phy.ofdm.channel_estimation.BaseChannelInterpolator | None = None, precision: Literal['single', 'double'] | None = None, device: str | None = None, **kwargs)

Bases: sionna.phy.block.Block

Abstract block for implementing an OFDM channel estimator.

Any block that implements an OFDM channel estimator must implement this class and its estimate_at_pilot_locations() abstract method.

This class extracts the pilots from the received resource grid y, calls the estimate_at_pilot_locations() method to estimate the channel for the pilot-carrying resource elements, and then interpolates the channel to compute channel estimates for the data-carrying resource elements using the interpolation method specified by interpolation_type or the interpolator object.

Parameters:

• resource_grid (sionna.phy.ofdm.resource_grid.ResourceGrid) – Resource grid

• interpolation_type (str) – The interpolation method to be used. It is ignored if interpolator is not None. Available options are NearestNeighborInterpolator ("nn"), LinearInterpolator without ("lin") or with averaging across OFDM symbols ("lin_time_avg").

• interpolator (BaseChannelInterpolator | None) – An instance of BaseChannelInterpolator, such as LMMSEInterpolator, or None. In the latter case, the interpolator specified by interpolation_type is used. Otherwise, the interpolator is used and interpolation_type is ignored.

• 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:

• y – [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size], torch.complex. Observed resource grid.

• no – [batch_size, num_rx, num_rx_ant] or only the first n>=0 dims, torch.float. Variance of the AWGN.

Outputs:

• h_hat – [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, fft_size], torch.complex. Channel estimates across the entire resource grid for all transmitters and streams.

• err_var – Same shape as h_hat, torch.float. Channel estimation error variance across the entire resource grid for all transmitters and streams.

Examples

import torch
from sionna.phy.ofdm import ResourceGrid, LSChannelEstimator

rg = ResourceGrid(num_ofdm_symbols=14,
                  fft_size=64,
                  subcarrier_spacing=30e3,
                  num_tx=2,
                  num_streams_per_tx=2,
                  pilot_pattern="kronecker",
                  pilot_ofdm_symbol_indices=[2, 11])

estimator = LSChannelEstimator(rg, interpolation_type="lin")

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

h_hat, err_var = estimator(y, no)
print(h_hat.shape)
# torch.Size([16, 1, 4, 2, 2, 14, 60])

Methods

abstractmethod estimate_at_pilot_locations(y_pilots: torch.Tensor, no: torch.Tensor) → Tuple[torch.Tensor, torch.Tensor]

Estimate the channel for the pilot-carrying resource elements.

This is an abstract method that must be implemented by a concrete OFDM channel estimator that implements this class.

Parameters:

• y_pilots (torch.Tensor) – [batch_size, num_rx, num_rx_ant, num_tx, num_streams, num_pilot_symbols], torch.complex. Observed signals for the pilot-carrying resource elements.

• no (torch.Tensor) – [batch_size, num_rx, num_rx_ant] or only the first n>=0 dims, torch.float. Variance of the AWGN.

Outputs:

• h_hat – [batch_size, num_rx, num_rx_ant, num_tx, num_streams, num_pilot_symbols], torch.complex. Channel estimates for the pilot-carrying resource elements.

• err_var – Same shape as h_hat, torch.float. Channel estimation error variance for the pilot-carrying resource elements.