Pilot-Aided Joint CFO and Doubly-Selective Channel Estimation for OFDM Transmissions
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Bibliographic record
Abstract
This paper studies the problem of pilot-aided joint carrier frequency offset (CFO) and channel estimation using Fisher and Bayesian approaches in orthogonal frequency division multiplexing (OFDM) transmissions over time- and frequency-selective (doubly selective) channels. In particular, the recursive-least-squares (RLS) and maximum-likelihood (ML) techniques are used to facilitate the Fisher estimation implementations. For the Bayesian estimation, the maximum-a-posteriori (MAP) principle is employed in formulating the joint estimation problem. With known channel statistics, the MAP-based estimation is expected to provide better performance than the RLS- and ML-based ones. To avoid a possible identifiability issue in the joint estimation problem, various basis expansion models (BEMs) are deployed as fitting parametric models for capturing the time-variation of the channels. Numerical results and related Bayesian Cramér Rao bounds (BCRB) demonstrate that the deployment of BEMs is able to alleviate performance degradation in the considered estimation techniques using the conventional block-fading assumption over time-varying channels. Among the considered schemes, the MAP-based estimation using the discrete prolate spheroidal (DPS) or Karhuen Loève (KL) basis functions would be the best choice that can provide mean-squared-error (MSE) performance comparable to BCRB in low signal-to-noise ratio (SNR) conditions (e.g., coded OFDM transmissions).
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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 | 0.001 |
| 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