MIMO-OFDM Suite#

This experimental suite provides a runnable 2×2 frequency-selective MIMO-OFDM channel-estimation protocol. The learned-estimator template uses a mixture of Sionna 3GPP TDL-A/C/E channels while retaining explicit example, receive-antenna, transmit-antenna, and subcarrier axes. Orthogonal comb pilots produce noisy observations, and Noema materializes sparse divided pilots, their mask, and an LS/interpolation fallback for portable learned estimators.

Benchmark Pack#

  • benchmarks/mimo_ofdm/channel_estimation_v1.yaml

The pack compares:

  • recipes/mimo_ofdm_ls_channel_estimation.yaml as an interpolation-aware LS baseline;

  • recipes/mimo_ofdm_adapter_channel_estimation.yaml as the mixed-profile LS/fixed-prior-LMMSE/portable-learned endpoint.

2×2 Sionna 3GPP TDL-A/C/E channel truth
  -> orthogonal comb-pilot grid
  -> SNR-controlled noisy pilot observation
  -> LS/interpolation, fixed-prior LMMSE, or portable learned endpoint
  -> NMSE and post-ZF spectral-efficiency evaluation

Both recipes declare mimo_ofdm_channel_estimation, which distinguishes this pilot-grid spine from the flat/SISO pilot_channel_estimation profile without turning antenna or pilot counts into profiles.

Adapter Point#

model.channel_estimator_adapter is a selectable Train/replace boundary. Its portable ONNX ABI receives sparse divided pilot observations, their binary mask, an operation-owned LS channel estimate, and noise variance. It returns an estimated complex channel tensor of the same shape. Channel truth remains an offline capture target.

The complete training and benchmark workflow is Learned 2×2 MIMO-OFDM channel estimation.

Metrics and Plots#

Core metrics:

  • mimo.channel_estimation.nmse;

  • mimo.channel_estimation.nmse_db;

  • mimo.channel_estimation.zf_spectral_efficiency_bps_hz;

  • mimo.channel_estimation.zf_rate_retention;

  • channel_estimation.nmse;

  • task.score;

  • channel.snr_db.

Default plot:

uv run noema benchmark run benchmarks/mimo_ofdm/channel_estimation_v1.yaml
uv run noema benchmark plot <result_id> --plot graceful-degradation --x channel.snr_db --y task.score --group method --out figures/mimo_ofdm_channel_estimation.png

Boundary#

This is a channel-estimation benchmark, not a complete 5G NR conformance test. The post-ZF metric measures the downstream sensitivity of the estimate; it does not add a full coded waveform, standardized DMRS, or BER/BLER claim.