Bibliographic record
Abstract
A per-survivor processing (PSP) maximum likelihood sequence detection (MLSD) receiver is developed for a fast time-varying frequency-selective Rayleigh fading channel with coloured additive noise, which follows an autoregressive (AR) model with unknown parameters. The correlation between noise samples is exploited to considerably enhance the performance of the communications. The maximum likelihood criterion is employed based on unknown noise parameters. This criterion has some desired properties, e.g. it has a unique joint minimum at the true values of the channel and the noise parameters. The new PSP–MLSD algorithm detects the input data and jointly estimates the noise and the channel parameters all together. The proposed structure can be viewed as a traditional PSP–MLSD receiver combined with an adaptive whitening filter. In a coloured noise environment, this scheme offers a faster tracking property, more accurate estimation of the channel and a substantially lower error probability compared with the traditional PSP–MLSD structure. The signal-to-noise ratio (SNR) improvement achieved by the proposed receiver, which can be called the noise whitening gain (NWG), is almost equal to the ratio of the energy of the additive noise to the energy of the unpredictable noise component. The square of the NWG gives also an accurate approximation for the bit error rate (BER) improvement ratio obtained by using the proposed algorithm compared with the traditional one.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".