Bayesian Joint Estimation of CFO and Doubly Selective Channels in MIMO-OFDM Transmissions
Bibliographic record
Abstract
This paper studies the problem of pilot-aided joint carrier frequency offset (CFO) and channel estimation using a Bayesian approach in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) transmissions over time- and frequency-selective (doubly selective) channels. Unlike the joint CFO and channel impulse response (CIR) estimation over block-fading channels, the joint CFO and time-variant CIR estimation gives rise to the identifiability problem where the number of observations (received samples) is smaller than that of both CFO and time-variant CIR parameters to be estimated. To reduce a large number of the time-variant CIR parameters to be estimated, various basis expansion models (BEMs) are deployed as fitting parametric models for capturing the time variation of the MIMO channels. As the main purpose of using BEMs, the resulting dimension reduction in the time-variant channel representation helps to avoid the identifiability issue in the joint estimation problem. Under Bayesian estimation, CFO and BEM coefficients are treated as random variables to be estimated by the maximum-a-posteriori (MAP) technique. Numerical results demonstrate that the deployment of BEMs is able to alleviate performance degradation in the considered estimation technique using the conventional assumption of block fading over time varying channels.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".