Row-Monomial Distributed Orthogonal Space-Time Block Codes with Channel Phase Information
Bibliographic record
Abstract
Very recently, we proposed the row-monomial distributed orthogonal space-time block codes (DOSTBCs) in [1] and showed that the codes achieved approximately twice higher bandwidth efficiency than the repetition-based cooperative strategy. In [1], we assumed that the relays did not have any channel state information (CSI) of the channels from the source to themselves, i.e. the channels of the first hop. However, we notice that this CSI can be readily obtained at the relays without any additional pilot signals or any feedback overhead. Therefore, in this paper, we assume that the relays have partial CSI of the first hop and use this information to construct the codes. We refer to those codes as the row-monomial DOSTBCs with channel phase information (DOSTBCs-CPI) and derive an upper bound of the data-rate of the codes. This upper bound suggests that the row-monomial DOSTBCs-CPI have higher bandwidth efficiency than the row- monomial DOSTBCs in [1], especially in a cooperative network with many relays. Furthermore, we find the actual row-monomial DOSTBCs-CPI achieving the upper bound of the data-rate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".