MFEqualizer

class sionna.phy.ofdm.MFEqualizer(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)

Bases: sionna.phy.ofdm.equalization.OFDMEqualizer

MF equalization for OFDM MIMO transmissions.

This block computes matched filter (MF) equalization for OFDM MIMO transmissions. The OFDM and stream configuration are provided by a ResourceGrid and StreamManagement instance, respectively. The detection algorithm is the mf_equalizer(). The block computes soft-symbol estimates together with effective noise variances for all streams which can, e.g., be used by a Demapper to obtain LLRs.

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:

• y – [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size], torch.complex. Received OFDM resource grid after cyclic prefix removal and FFT.

• h_hat – [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], torch.complex. Channel estimates for all streams from all transmitters.

• err_var – [Broadcastable to shape of h_hat], torch.float. Variance of the channel estimation error.

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

Outputs:

• x_hat – [batch_size, num_tx, num_streams, num_data_symbols], torch.complex. Estimated symbols.

• no_eff – [batch_size, num_tx, num_streams, num_data_symbols], torch.float. Effective noise variance for each estimated symbol.