Minimum ber block precoders for zero-forcing equalization
Bibliographic record
Abstract
We determine the linear precoder that minimizes the bit error rate (BER) at moderate-to-high signal-to-noise ratios (SNRs) for block transmission systems with zero-forcing (ZF) equalization and threshold detection. The design is developed for the two standard schemes for eliminating inter-block interference, viz, zero padding (ZP) and cyclic prefix (CP). We show that both the ZP minimum BER precoder and the CP minimum BER precoder provide substantially lower error rates than standard block transmission schemes, such as orthogonal frequency division multiplexing (OFDM). The corresponding SNR gains can be on the order of several decibels. We also show that the CP minimum BER precoder can be obtained by a two-stage modification of the water-filling discrete multitone modulation (DMT) scheme in which the diagonal water-filling power loading is replaced by a full matrix consisting of a diagonal minimum mean square error power loading matrix post multiplied by a discrete Fourier transform (DFT) matrix.
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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) | 0.003 | 0.001 |
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".