A simple and efficient timing offset estimation for OFDM systems
Bibliographic record
Abstract
A simple timing offset estimation method for orthogonal frequency division multiplexing (OFDM) systems as a modification to Schmidl and Cox's method (see IEEE Trans. on Comms., vol.45, no.12, p.1613-21, 1997) is presented. By designing the training symbol, the timing metric plateau inherent of Schmidl et al. is eliminated and hence the performance is improved. The performances of the proposed method and of Schmidl et al. are evaluated by computer simulation in terms of the estimator variance. The timing offset estimator of Landstrom, Wilson, van de Beek, Odling and Borjesson (see Proc. Intl. Conf. on Communications, Vancouver, BC, Canada, p.500-5, 1999) is also included in the performance comparison as another reference. The simulation results show that the proposed method achieves significantly smaller estimator variance. Using more samples in calculation of the half symbol energy required in the timing metric is shown to give more robustness against a dispersive channel.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.002 | 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".