Bibliographic record
Abstract
Multiple-symbol differential detection (MSDD) is a robust maximum-likelihood (ML) receiver technique for frequency-nonselective fast Rayleigh fading channels. However, its complexity grows exponentially with the block size N and that makes it impractical to implement. Recently, multiple-symbol differential sphere decoder (MSDSD) is developed to alleviate this problem but its complexity at low signal-to-noise ratio (SNR) grows exponentially with decreasing SNR. In this paper, we explore the potential of the Fano algorithm as an efficient MSDD receiver. The bit-error performance and the complexity of the Fano-MSDD are evaluated and compared with other reduced complexity techniques. Our preliminary results indicate that Fano-MSDD is more attractive than decision-feedback differential detection (P.Y. Kam and CH Teh, 1983) and (R Schober and WH Gerstacker, 2000) from both the error performance and the complexity points of view. When compared to the MSDSD, the Fano-MSDD suffers a moderate degradation in power efficiency. However, its computational complexity is very steady even at low SNR. This translates into a dramatic saving in complexity over the MSDSD when SNR is low. Even at large SNR, the Fano-MSDD still provides a small edge over the MSDSD in terms of complexity. We believe that with some fine tuning of the decoder parameters, such as the bias and the threshold's step size, it is possible to extract better performance from the Fano decoder than it is now.
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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 | 0.008 |
| 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 | 0.000 |
| 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".