A Postfix Synchronization Method for OFDM and MIMO-OFDM Systems
Bibliographic record
Abstract
This paper proposes a synchronization method that uses as preamble two identical training parts (Chu sequences) followed by a flipped postfix (FlP) for OFDM and MIMO-OFDM systems. The proposed method provides symbol timing and carrier frequency offset (CFO) estimators. By using this FlP-based preamble, the accuracy of the symbol timing estimator is highly improved without increasing the computation complexity. The time synchronization method includes both coarse and fine time synchronization. In particular, this paper indicates how to set up the threshold for the fine time synchronization. Performance evaluation of the proposed method, applied to MIMO-OFDM systems, with various diversity combining shows that receive diversity gives more gain than transmit diversity. The proposed method for both OFDM and MIMO-OFDM systems is also compared to various synchronization methods such as the Schmidl-Cox (SC) and Minn-Zeng-Bhargava (MZB)'s methods in time invariant and time-variant Rayleigh fading channels in terms of mean and variance of the timing and CFO estimators. Overall, it is seen that the proposed new method offers good advantages compared to existing synchronization methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".