Decision-Feedback Subset Multiple-Symbol Differential Detection for Unitary Space-Time Modulation
Bibliographic record
Abstract
In this paper, a novel noncoherent detection algorithm for differential space-time modulation (DSTM) over flat-fading multiple-input-multiple-output channels is presented. This algorithm, which is referred to as decision-feedback subset multiple-symbol differential detection (DF-S-MSDD), combines ideas from decision-feedback differential detection (DFDD) and subset multiple-symbol differential detection (S-MSDD). More specifically, the DF-S-MSDD decision metric includes a number of previous decisions (i.e., decision feedback), and the optimization over the remaining hypothetical symbols returns decisions only on a subset of these symbols (i.e., S-MSDD). Furthermore, an implementation of DF-S-MSDD based on tree-search (TS) methods is devised. Due to the concept of subset detection, DF-S-MSDD outperforms MSDD in terms of error-rate performance. At the same time, due to the use of decision feedback, it also requires lower computational complexity than the TS-based MSDD schemes for the DSTM recently proposed in the literature.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".