Applications of Basis Selection Algorithms in Communication Problems
Bibliographic record
Abstract
In this paper, we study the application of sequential basis selection (SBS) algorithms in two different communication problems. These problems represent different cases in terms of the structure of their set of equations. The two considered cases are; undercomplete set of equations (sparse channel estimation problem) and overcomplete set of equations. These cases are carefully selected in order to demonstrate that SBS algorithms can be applied to both types of equations. The basic matching pursuit (BMP) and the orthogonal matching pursuit (OMP) algorithms are selected as the SBS algorithms. In sparse channel estimation problem, the BMP and the OMP algorithms are compared with the least square channel estimates and the minimum variance unbiased estimates (MVUE). It is shown that the OMP algorithm gives estimates that are almost converging to MVUE. In angle of arrival (AOA) detection problem, the detection performances of the BMP and OMP algorithms are compared with the well known MUSIC algorithm and the Cramer Rao bounds. It is shown that their performances exceed that of MUSIC for correlated signals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".