Joint estimation of channel response, frequency offset, and phase noise in OFDM
Bibliographic record
Abstract
Accurate channel estimates are needed in orthogonal frequency-division multiplexing (OFDM), and easily obtained under the assumption of perfect phase and frequency synchronization. However, the practical receiver encounters nonnegligible phase noise (PHN) and carrier frequency offset (CFO), which create substantial intercarrier interference that a conventional OFDM channel estimator cannot account for. In this paper, we introduce an optimal (maximum a posteriori) joint estimator for the channel impulse response (CIR), CFO, and PHN, utilizing prior statistical knowledge of PHN that can be obtained from measurements or data sheets. In addition, in cases where a training symbol consists of two identical halves in the time domain, we propose a variant to Moose's CFO estimation algorithm that optimally removes the effect of PHN with lower complexity than with a nonrepeating training symbol. To further reduce the complexity of the proposed algorithms, simplified implementations based on the conjugate gradient method are also introduced such that the estimators studied in this paper can be realized efficiently using the fast Fourier transform with only minor performance degradation. EDICS: SPC-MULT, SPC-CEST, SPC-DETC
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".