Bayesian EM-based demodulators for frequency-selective fading channels
Bibliographic record
Abstract
In this paper, the problem of adaptive MAP symbol detection in the uncoded transmission as well as the problem of adaptive APP demodulation in the coded transmission of data symbols over the frequency-selective Rayleigh fading channel are explored within the framework of the Bayesian expectation-maximization (BEM) algorithm. In particular, two novel versions of the BEM-based detection and demodulation algorithms are derived. In contrast to the earlier developments of BEM algorithms, the formulations derived in this paper lead to the computationally efficient algorithms which avoid the matrix inversions while using sequential processing over the time and trellis branch indexes. In addition, it is shown how the recursive versions of the BEM algorithms can be combined with the well-known forward-backward processing soft-input soft-output (SISO) algorithms resulting in adaptive SISOs with soft decision directed (SDD) channel estimators. An application of the proposed algorithms to the iterative "turbo-processing" receivers illustrates how these SDD channel estimators can efficiently exploit the extrinsic information obtained from the SISO decoder in order to enhance their estimation accuracy.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".