Decentralized distributed space-time trellis coding
Bibliographic record
Abstract
In this paper, we introduce a new class of distributed space-time trellis codes (DSTTCs) for wireless networks with a large set of decode-and-forward relay nodes N. We consider the general case where each relay node is equipped with NTantennas and the destination node is equipped with NRantennas. It is assumed that at any given time only a small, a priori unknown subset of nodes S ∈ N is active and neither the relay nodes nor the destination node know which relay nodes are active. In the proposed scheme, each node is assigned a signature matrix and the signal transmitted by an active node is the product of the signature matrix and a space-time trellis code (STTC) matrix originally designed for Ncco-located antennas. It is shown that existing full-rank STTCs designed for Nc⩾ 2 co-located antennas are a favorable choice for the code matrix. Two practical designs for good deterministic signature matrix sets are provided and compared with random signature matrices. If the signature matrices are properly optimized, the proposed DSTTCs achieve a diversity order of d = min{NcNR, NTNSNR} if NSnodes are active. Simulation results confirm that DSTTCs yield significant performance gains over distributed space-time block codes and distributed space-time filtering. Furthermore, equipping relay nodes with a second antenna can be highly beneficial especially if only few nodes can be active at any given time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".