Blind identification of FIR systems driven by Markov-like input signals
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Bibliographic record
Abstract
We propose a new algorithm for the blind identification and equalization of finite impulse response (FIR) systems using the second-order statistics of the received signal. The new algorithm is set in the same context as the algorithms of Tong et al. (1994) and Moulines et al. (1995), however, unlike those earlier approaches it is designed to allow correlated input signals. Specifically, the algorithm accommodates finite memory sources and sources whose autocorrelation function decays exponentially. Numerical simulations compare the equalization performance of the new algorithm to those of Tong and Moulines. It is shown that our algorithm yields consistently lower bit-error rates at a wide variety of signal-to-noise ratios and at various equalizer lengths. Moreover, the algorithm maintains this advantage even if it has no a priori information of source correlation or if source symbols are uncorrelated.
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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 | 0.000 |
| 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