Optimization of distributed space-time filtering
Bibliographic record
Abstract
Distributed space-time filtering (DSTF) has been re- cently proposed in (1) to increase the diversity in cooperative wire- less networks. In DSTF each node in the network is assigned a signature filter vector (SFV) and nodes cooperate without know- ing their partners. In this paper, we derive an approximation for the pairwise error probability of DSTF and use the result- ing expression for the optimization of SFVs. Two methods are provided for efficient filter calculation. Furthermore, unlike the original DSTF scheme in (1), we do not require the number of ac- tive nodes to be equal to the length of the SFVs, which is shown to result in additional performance gains. Simulation results show that the novel SFV designs outperform the previously proposed design even if non-ideal effects such as suboptimum equalization, imperfect channel estimation, and imperfect timing synchroniza- tion are taken into account.
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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) | 0.001 | 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 | 0.000 |
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 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".