Distributed Space-Time Transmission with CPM
Bibliographic record
Abstract
In this paper, a class of distributed space-time (ST) codes for continuous-phase modulation (CPM) is introduced. The distributed ST codes are designed to operate in wireless networks containing a large set of nodes N , of which only a small a priori unknown subset S sub N will be active at any time. Under the proposed scheme, a relay node transmits a signal which is the product of a diagonal block-based ST (DBST) code (optimized specifically for ST-CPM transmission) and a signature vector of length Nc uniquely assigned to each node in the network. Two efficient methods are presented for the design and optimization of signature vector sets. If a properly designed signature vector set is employed it is shown that a diversity d = min{Ns,Nc} can be obtained, where Nsis the number of active users. Further, the decoding complexity of the proposed scheme is shown to be independent of the number of active relay nodes. Through the combination of DBST-CPM codes and signature vector sets the proposed distributed DBST-CPM codes allow for power-efficient cooperative transmission, and low complexity coherent and noncoherent receiver implementations.
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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 | 0.001 | 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".