Iterative Receiver Design With Joint Doubly Selective Channel and CFO Estimation for Coded MIMO-OFDM Transmissions
Bibliographic record
Abstract
This paper is concerned with the problem of turbo (iterative) processing for joint channel and carrier frequency offset (CFO) estimation and soft decoding in coded multiple-input-multiple-output (MIMO) orthogonal frequency-division-multiplexing (OFDM) systems over time- and frequency-selective (doubly selective) channels. In doubly selective channel modeling, a basis expansion model (BEM) is deployed as a fitting parametric model to reduce the number of channel parameters to be estimated. Under pilot-aided Bayesian estimation, CFO and BEM coefficients are treated as random variables to be estimated by the maximum a posteriori technique. To attain better estimation performance without sacrificing spectral efficiency, soft bit information from a soft-input-soft-output (SISO) decoder is exploited in computing soft estimates of data symbols to function as pilots. These additional pilot signals, together with the original signals, can help to enhance the accuracy of channel and CFO estimates for the next iteration of SISO decoding. The resulting turbo estimation and decoding performance is enhanced in a progressive manner by benefiting from the iterative extrinsic information exchange in the receiver. Both extrinsic information transfer chart analysis and numerical results show that the iterative receiver performance is able to converge fast and close to the ideal performance using perfect CFO and channel estimates.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".