New joint frame synchronisation and carrier frequency offset estimation method for OFDM systems
Bibliographic record
Abstract
Abstract We propose a new joint frame synchronisation and carrier frequency offset estimation scheme for burst transmission mode OFDM systems. This scheme uses a central‐symmetric and comb‐like (CSCL) training sequence, which eases the power detection at the receiver without increasing the total training sequence power. Fine frame synchronisation as well as carrier frequency offset acquisition with a maximum acquisition range of $\pm {{N} \over {4\times {\rm SF}}}$ times the sub‐carrier spacing can also be performed based on the proposed CSCL training sequence, where N is the discrete Fourier transform (DFT) length and SF is an integer‐valued spreading factor used to generate CSCL. The post‐acquisition residual carrier frequency offset can be further estimated and corrected via a fine adjustment algorithm. In order to reduce performance loss due to the high peak‐to‐average power ratio (PAPR) of the CSCL training sequence, a time‐domain constant‐envelope (CE) training sequence is also proposed. The superior estimation accuracy of the proposed algorithm over that of the Moose algorithm and the SS (Shi and Serpedin) algorithm is proved by computer simulation. Copyright © 2008 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".