Capacity-optimal structured linear dispersion codes for correlated MIMO channels
Bibliographic record
Abstract
In this paper, our analysis is based on a unitarily equivalent eigen-domain representation of correlated MIMO fading channels. The eigen-domain channel matrix has statistically independent entries and the non-uniform powers of its entries capture the channel correlation structure. Capacity and pairwise error probability (PEP) analysis is greatly simplified in the eigen-domain. In particular, the capacity-achieving input covariance matrix is diagonal in the eigen-domain, and the PEP bounds reveal the interaction between the code and the channel in spatio-temporal signal space dimensions. Furthermore, the achievable spatial multiplexing gain and diversity are constrained by the number of dominant channel entries in the eigen-domain. Using insights from the capacity and PEP analysis, we propose a characterization of capacity-optimal linear dispersion codes via a family of structured code generator matrices. These are parameterized by three unitary matrices, that determine the space-time structure of the codes, and a diagonal power-shaping matrix. The role of these matrices in controlling code performance is discussed and illustrative numerical results are presented.
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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".