Weighted circularly convolved filtering in OFDM/OQAM
Bibliographic record
Abstract
A novel technique for removing the edge time transitions, a.k.a. tails, as well as the half symbol offset overhead of OFDM/OQAM signal is proposed. The proposed technique, which is called weighted circular convolution, totally removes the signal overheads, while preserving the real-orthogonality of the prototype filter, and as such, does not incur any ISI/ICI on the demodulated OQAM symbols. It is based on extension of the finite-length OQAM sequence to infinite-length such that the resulting signal is periodic. This periodicity together with special structure of OFDM/OQAM enables one to transmit only the desired overhead-removed portion of signal. This is equivalent to a weighted circular convolution instead of linear convolution in the OFDM/OQAM modulator/demodulator. The spectral sidelobe augmentation due to the sharp edge transitions of the overhead-removed signal is resolved by a weighted time-domain windowing. The proposed overhead-removal technique is applicable to an OFDM/OQAM burst of arbitrary length (either even or odd) and, among other benefits, significantly improves the spectral efficiency especially for short-burst signals.
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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 | 0.001 |
| 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".