Joint Carrier Frequency Offset, Sampling Time Offset and Channel Estimation for OFDM-OQAM Systems
Bibliographic record
Abstract
Among the alternative multicarrier modulation techniques to orthogonal frequency division multiplexing (OFDM), a derivative of OFDM based on offset quadrature amplitude modulation (OFDM-OQAM) has been one of the most prominent to alleviate the sensitivity problem of the former to timing and frequency mismatch. In this paper, we propose an improved joint estimation method for carrier frequency offset (CFO), sampling time offset (STO) and channel impulse response (CIR) in OFDM-OQAM systems. The proposed method instruments a data-aided maximum-likelihood (ML) joint estimation of the unknown parameters, as derived under an assumption of Gaussian noise and independent input symbols by splitting the interference into pilot, non-pilot and noise terms. Performance evaluation is carried out through simulations by comparing the proposed method with a highly-cited previous work which considers all the three types of parameters in one development. The improvements in the results indicate the superiority of the proposed joint ML-based estimator.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".