Frequency-Domain Space-Time Precoders for Severe Time-Dispersive Channel Employing Single-Carrier Modulations
Bibliographic record
Abstract
Single carrier (SC) modulations employing frequency domain equalization (FDE) have been shown to be appropriate for severe time-dispersive channels, having similar or better performances than OFDM modulations, while offering the same complexity and lower peak-to-average power ratio. In this paper we introduce a class of low complexity linear frequency domain precoders (multiple-beamformers) for spatially correlated frequency selective multiple-input-multiple-output MIMO systems employing SC modulations. The specific designs targets minimization of a lower bound on the mean-square-error (MSE) under the assumption that only second order statistics of channel (covariance matrix of channel and noise) is available at the transmitter. These precoders are designed for different wideband receivers such as BLAST, MIMO-DFE, linear FDE. The proposed designs are assessed and compared for relatively large correlations and severely frequency selective channels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".