Novel rotation angle for quasi-orthogonal space-time block codes
Bibliographic record
Abstract
Recently, various space-time block coding (STBC) schemes have been developed to take advantage of the MIMO communication channel. The code designs using the pair-wise error probability of the maximum likelihood (ML) detector are based mainly on the rank and the determinant criteria. In particular, the current STBC designs focus on full diversity and the non-vanishing determinant, since such codes enable the optimal tradeoff of diversity and multiplexing gains. In this paper, we consider a coherent communication system equipped with multiple transmitter antennas and a single receiver antenna, i.e., a MISO system. For such systems, Afarkhani, Tirkkonen-Boariu-Hottinen, and Papadias-Foschini proposed the quasi-orthogonal STBC designs with fast ML decoding. Su and Xia designed the rotated quasi-orthogonal STBCs enabling full diversity and optimal coding gain. However, the nearest neighbor number per symbol for this code tends to infinity when the size of constellation is large. Here, we explore a novel criterion to design rotated quasi-orthogonal STBCs. In addition to both maximizing the rank and the coding gain, our design attempts to make the average number of the nearest neighbors as small as possible.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".