Development of a post-detection equalization technique for multicarrier modulation/demodulation systems
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Bibliographic record
Abstract
This paper is concerned with the development of a post-detection equalization technique for filterbank-based multicarrier modulation/demodulation systems. This technique is based on the equalization of the channel fractional delay in each subchannel in time synchronization with the constituent decimator at the receiver end, achieved through the exploitation of a subset of the signal samples at the input of the decimator. The resulting equalization gives rise to a high signal-to-noise ratio while requiring a short equalizer length. Moreover, it permits a tradeoff between various equalization parameters, leading to high computational flexibility. The search for an optimal solution can be constrained to within the channel lower and upper group-delay bounds within each subchannel, significantly simplifying the equalizer training.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it