Optimum Design of Differential Unitary Space-Time Modulation
Bibliographic record
Abstract
In this paper, we propose an extended class of unitary signal constellations for differential unitary space-time modulation (DUSTM). We also derive an approximation of the upper bound on the symbol error probability (SEP) as a general criterion to find the optimum codes. This criterion is valid for both group or non-group constellations. For asymptotically high or low signal-to-noise ratio (SNR), signal-constellation parameters are usually determined based on the rank-and-determinant (diversity product) or the Euclidean distance (diversity sum) criterion. Since both these criterion are SNR-independent, the search results are not necessarily optimum parameters in medium to low SNRs. Thus instead of using diversity sum or product, we search for the constellation parameters to minimize the union-bound based criterion, taking into account the number of receive antenna and the operation SNR. Simulation results show that the constellations optimized for the union-bound based criterion outperform the previous codes resulting from rank-and- determinant (diversity product) or Euclidean distance (diversity sum).
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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".