Adaptive MLSD receiver employing noise correlation
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it