Joint Channel and Frequency Offset Estimation for Oversampled Perfect Reconstruction Filter Bank Transceivers
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Bibliographic record
Abstract
Recently, DFT-based oversampled perfect reconstruction filter banks (OPRFB), as a special form of filtered multitone, have shown great promises for applications to multicarrier modulation. Still, accurate frequency synchronization and channel equalization are needed for their reliable operation in practical scenarios. In this paper, we first derive a data-aided joint maximum likelihood (ML) estimator of the carrier frequency offset (CFO) and the channel impulse response (CIR) for OPRFB transceiver systems operating over frequency selective fading channels. Then, by exploiting the structural and spectral properties of these systems, we are able to considerably reduce the complexity of the proposed estimator through simplifications of the underlying likelihood function. The Cramer Rao bound on the variance of unbiased CFO and CIR estimators is also derived. The performance of the proposed ML estimator is investigated by means of numerical simulations under realistic conditions with CFO and frequency selective fading channels. The effects of different pilot schemes on the estimation performance for applications over time-invariant and mobile time-varying channels are also examined. The results show that the proposed joint ML estimator exhibits an excellent performance, where it can accurately estimate the unknown CFO and CIR parameters for the various experimental setups under consideration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it