Multiple-Symbol Differential Sphere Decoding
Bibliographic record
Abstract
In multiple-symbol differential detection (MSDD) for power-efficient transmission over Rayleigh fading channels without channel state information, blocks of N received symbols are jointly processed to decide on N-1 data symbols. The search space for the maximum-likelihood (ML) estimate is therefore (complex) (N-1)-dimensional, and maximum-likelihood MSDD (ML-MSDD) quickly becomes computationally intractable as N grows. Mackenthun's low-complexity MSDD algorithm finds the ML estimate only for Rayleigh fading channels that are time-invariant over an N symbol period. For the general time-varying fading case, however, low-complexity ML-MSDD is an unsolved problem. In this letter, we solve this problem by applying sphere decoding (SD) to ML-MSDD for time-varying Rayleigh fading channels. The resulting technique is referred to as multiple-symbol differential sphere decoding (MSDSD).
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".