Feedforward symbol timing recovery technique using two samples per symbol
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a new feedforward symbol timing recovery technique using timing estimation followed by interpolation is presented for digital receivers with two samples/symbol or higher sampling rate. A few timing estimation algorithms are proposed to estimate the timing offset accurately. The basic algorithm uses only the in-phase (I) or quadrature (Q) signal for timing detection, which applies to a BPSK communication system. It is shown that the basic algorithm, when applied in quadrature modulation systems where both I and Q signals are available, can be modified slightly to yield an improved estimation precision. The mean and variance of the resulting timing estimate are analyzed and simulated, supporting a satisfactory estimation performance. It is also shown that by applying a postprocessing scheme, such as the Kalman filter, the variance can be further reduced, resulting in a smoothed timing estimate. Some of the issues concerning the implementation of the proposed technique are also addressed.
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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 | 0.000 |
| 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 it