Design of linear dispersion codes: asymptotic guidelines and their implementation
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Bibliographic record
Abstract
In this paper, a design method is developed for the class of linear-dispersion (LD) codes - a diverse set of space-time codes that subsumes several standard designs. The development begins by showing that for systems that employ a large number of transmit antennas, LD codes constructed from unitary coding matrices are asymptotically optimum from different design perspectives, viz., minimum mean square error (MMSE), mutual information, and average pairwise error probability (PEP). Those measures have a direct impact on the detection complexity, data rate, and error performance that a space-time code can achieve. Using the insight generated by the asymptotic result, a structured design technique for the LD coding matrices, that suits a broad class of configurations is provided. The resulting codes can support high data rates and provide performance advantages over current designs when decoded with a standard detector. Based on the asymptotic results, a row interleaving scheme is proposed, and it is shown to result in significant performance enhancement.
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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 | 0.001 | 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 it