Short-block equalization techniques employing channel estimation for fading time-dispersive channels
Bibliographic record
Abstract
The objective of the present study is reliable digital communications over fading channels with severe time-dispersion. The authors present three short-block equalization techniques based on linear, nonlinear decision-directed, and maximum-likelihood estimation principles. Short alternating blocks of data and training symbols are used. In contrast to the recursive symbol-by-symbol equalization approaches usually employed, each data block is detected as a unit. These schemes require an estimate of the channel impulse response. This is considered an advantage from the adaptation point of view, since channel response estimation is one of the simplest adaptation requirements of any equalization process. Performance is evaluated for QPSK (quadrature phase-shift keying) and BPSK (binary phase-shift keying) signaling using a Rayleigh fading-channel model with severe time-dispersion. The degradation from ideal for the three schemes was about 5, 2.5, and 1 dB respectively.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".