OFDM carrier frequency offset correction using zero-crossings of the inter-carrier interference based cost function
Bibliographic record
Abstract
This paper introduces a carrier frequency offset (CFO) correction technique for Orthogonal Frequency Division Multiplexing (OFDM) by exploiting baseband characteristics of the Nyquist sampled received signal Fourier transform. The novelty of our algorithm is to capture the inter-carrier interference (ICI) effects using a cost function called CFO characteristic function (CF). The CFO CF is derived analytically by considering the structured nature of CFO effects. Using pilot data in an OFDM frame, we convert the matrix relation capturing the ICI effects in the OFDM signal to a functional representation of CFO. By finding numerically the root of this function, the CFO is estimated and consequently canceled. The computational complexity of the proposed receiver is significantly reduced by working with efficient numerical methods for root estimation. Robustness of the proposed ICI reduction is demonstrated at the expense of an increased computational complexity at the receiver, making this scheme attractive in practical communication systems.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".