Bibliographic record
Abstract
Time-selective signaling is an important technique for improving the effectiveness of traditional error correcting codes in data transmission systems that exhibit multipath fading. Such channels often arise in mobile wireless communications systems. The time-selective signaling method, which spreads the modulated data with a limited number of spreading sequences, like m-sequences, can be used for intersymbol interference cancellation at the receiver. The number of spreading sequences depends on the number of tabs considered for the frequency selective fading channels. Mathematically, it is shown that, from the perspective of the received signal, this algorithm can effectively transform an arbitrary frequency selective fading channel into a nonfading channel or marginally additive white Gaussian noise (AWGN) channel. Finally, we propose a low complexity turbo equalizer based on time-selective signaling which can be considered as an alternative to linear, nonlinear, and iterative turbo equalizers. Simulation results show that the proposed turbo equalizer is much less sensitive to SNR mismatch and channel estimation error variance than the conventional turbo MAP equalizer.
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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) | 0.003 | 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".