Bibliographic record
Abstract
Distributed space-time filtering (DSTF) facilitates node cooperation and achieves a diversity gain while not requiring node coordination, i.e., cooperating nodes do not have to be aware of their partners. This makes DSTF attractive for application in future sensor, ad hoc, and wireless networks. In this paper, we derive a novel cost function and related practical algorithms for optimization of the signature filter vectors (SFVs) used in DSTF. We extend DSTF to frequency-selective fading channels and compare its performance with that of delay diversity transmission with co-located antennas. For the special case of frequency-nonselective channels we show that SFV optimization is closely related to the signature sequence design for code-division multiple access (CDMA) systems and the design of Grassmannian frames. Numerical and simulation results show that the novel SFV designs perform significantly better than previously proposed designs even if non-ideal effects such as suboptimum equalization, imperfect channel estimation, and imperfect timing synchronization 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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".