Adopting ordered weighted averaging approach in multitaper temporal channel estimates to enhance channel capacity
Bibliographic record
Abstract
This letter proposes to collect the most of temporal channel information from the frequency domain by exploiting the diversity offered from the fusion of multiple windows, commonly known as tapers. For this, frequency-domain measurements are filtered out by using the selected windows, weighted, and then combined prior to the application of the inverse Fourier transform, thereby adopting a dual temporal concept of multitaper spectrum estimate technique. We have opted for multitaper principle due to the previous findings with the spectrum estimate technique that, with a unique window, more or less a significant amount of the original data is discarded, leading to the increase of the estimates variance, and causing a leakage of energy across frequencies. In this letter, the windows weights are selected by adopting ordered weighted averaging method, which involves the solution of a constrained nonlinear optimization problem, such as this one, with a given orness degree as the constraint, and the maximum entropy as the objective function. This concept has been successfully applied to our multiple-input-multiple-output underground channel measurements carried out in a mine in Northern Canada using channel sounding technique in the frequency domain.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".