EM-based sequential detection for mixed mode Ricean/Rayleigh fading channels with unresolved delays
Bibliographic record
Abstract
This paper considers a sub-optimal sequential non-coherent pilot-aided receiver based on the expectation-maximization (EM) algorithm for unresolved mixed mode Ricean/Rayleigh channels. The proposed detection technique iteratively finds the maximum likelihood (ML) estimate using symbol-by-symbol maximizations. It is shown that the EM-based scheme that uses a decorrelation matrix, yields diversity-like gains in the case of BPSK or 4QAM DS-CDMA signaling for various spreading gains even if the multipath is unresolved and the channel is highly correlated and only known statistically. Comparison with minimum mean square error (MMSE) schemes that assume instantaneous knowledge of the channel, showed the superiority of the EM-based structure over one single-sided tapped delay line MMSE (KS-MMSE) scheme and its very close performance to a double-sided tapped delay line MMSE approach (KD-MMSE) although the EM-based structure does not know the channel.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".