Bibliographic record
Abstract
We present in the paper a multiple-symbol differential detector (MSDD) for differential space-time (ST) codes in Rayleigh flat fading channel. The detector, termed a Fano ST-MSDD, uses the Fano algorithm as its decoding engine and is capable of delivering excellent error performance at moderate implementation complexity over a wide range of fading rates. Essentially, the detector is an "intelligent" decision feedback detector (DFD) that uses a running threshold and the accumulated path metric as navigation tools when it roams the decoding tree. In the static channel, our best Fano ST-MSDD scheme with a detection window size of N=6 is able to narrow the original 3 dB gap between ideal coherent and conventional ST differential detection by 1 dB. In a fast fading channel with a Doppler frequency of three percent the ST symbol rate, the error curve of our best TV-10 Fano ST-MSDD is able to "track" that of the ideal coherent detector (with a 3-dB gap in between) and there is no irreducible error floor. All these performances become even more remarkable when we consider the rather moderate implementation complexity reported in the paper. Because of its close relationship with a DFD, the Fano ST-MSDD has a similar complexity as the DFD at large signal-to-noise ratio (SNR). Actually, we found that the complexity of the Fano ST-MSDD is a relatively stable function of the SNR
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".