Design of linear dispersion codes: some asymptotic guidelines and their implementation
Bibliographic record
Abstract
The family of linear dispersion (LD) codes is a diverse set of space-time block codes that subsumes several standard designs. In this paper, we provide a design technique for LD codes that generates codes which enable large capacities and perform well when decoded with a standard suboptimal detector. Our design technique is motivated by the observation that for an independent Rayleigh fading channel, as the number of transmit antennas grows, LD codes with a certain orthogonal structure simultaneously approach a maximized upper bound on the capacity, and a minimized lower bound on a certain mean square error performance measure. Using this asymptotic result as a guide, we impose the orthogonal structure on finite sized codes and optimize the resulting system. Imposing this structure significantly simplifies the design procedure, and as we demonstrate via simulation, leads to codes that perform well in practice. Using insight generated by our study of the asymptotic properties of LD codes, we also propose a row interleaving scheme which is shown to result in significant performance enhancement.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".