On the Design of 2x2 Full-Rate Full-Diversity Space-Time Block Codes
Bibliographic record
Abstract
There has been considerable research on the design of space-time block codes (STBCs) that guarantee full diversity without sacrificing the data rate. The main challenge is to maximize the coding gain by maximizing the determinant criterion. It is shown that the most of previous STBCs with full rate and full diversity order (FRFD) (e.g. threaded algebraic space-time (TAST) codes) are constructed via a unitary generator matrix. However, the unitary matrix has been represented using only a small number parameters to enable algebraic code design. In this paper, for a 2 times 2 STBC, we use a more general unitary matrix with a large number of parameters for STBC design. We obtain an upper bound on the coding gain and show that the maximum coding gain is attainable only with PAM signaling. Since optimum parameters for the case of QAM signaling is analytically intractable, we search using the genetic algorithm (GA) method. We also use the union bound criterion for code parameter search by GA. Our simulation results show that with both criteria, the optimum code for QAM signaling is the Golden code. The proposed code significantly outperforms other existing STBCs with the gains about 2 dB at a symbol error rate of 10 for BPSK and 4-PAM. The proposed code performs identically to the Golden code for QAM.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".