Joint Synchronization and Channel Estimation for OFDM Transmissions over Doubly Selective Channels
Bibliographic record
Abstract
This paper proposes a pilot-aided joint channel estimation and synchronization scheme for burst-mode orthogonal frequency division multiplexing (OFDM) systems over time- and frequency-selective (doubly selective) channels. Based on the basis expansion model (BEM) for representing doubly-selective channels, a least-square (LS) cost function of carrier frequency offset (CFO) and BEM coefficients is formulated for the joint estimation problem. By applying the first-order Taylor series expansion, an approximately linearized estimation error is obtained to facilitate a recursive least-square (RLS)-based joint CFO and BEM estimation with the aid of pilot OFDM symbols. Simulation results demonstrate that, over a wide range of Doppler spreads, the proposed estimation scheme offers a high robustness against the time variation of fast fading channels and outperforms the linear minimum mean-square (LMMSE) algorithm using CFO estimates provided by the CFO estimation technique in.
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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 | 0.002 |
| 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 | 0.001 |
| 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 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".