An Efficient Channel Estimator for Frequency Hopping System via Propagator Method
Bibliographic record
Abstract
In this paper, the multi-path time delay estimation problem for a Slow Frequency Hopping (SFH) system using the Propagator Method (PM) is considered. Two novel techniques are proposed. The first technique is developed by applying the Propagator Method (PM) in association with the well-known MUSIC algorithm. Based on the proposed technique a highly efficient estimator has been achieved. The second technique is a simple closed-form expression which is obtained by applying PM and Eigen Value Decomposition (EVD) of the projection matrix. The proposed techniques generate estimates of the unknown parameters. Such estimates are based on the observation and/or covariance matrices. Moreover, the PM itself does not require the EVD or Singular Value Decomposition (SVD) of the Cross-Spectral Matrix (CSM) of received signals. As a result, a significant improvement in computational load is achieved. Computer simulations are also included to demonstrate the effectiveness of the proposed methods.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".