A Novel Framework for Signal Detection in Alpha-Stable Interference
Bibliographic record
Abstract
The design of detectors for binary signals in interference modeled using the symmetric α-stable distribution is considered. Since the optimal detector is impractically complex, many suboptimal detectors have been proposed such as the linear, soft limiter and Cauchy detectors. However, no adequate explanation for the difference in performance between these detectors has been proposed. In this paper, we propose a novel framework, based on the optimal decision regions, that is used to justify the performance of many suboptimal detectors and compare them to the optimal one. Moreover, the analysis of the framework provides a novel method to significantly improve the performance of the soft limiter detector by employing an adaptive threshold that is a function of the signal level and noise dispersion. As the number of samples per symbol increases, the performance of the proposed adaptive detector approaches the optimal performance at almost no additional complexity over the conventional linear (Gaussian) detector.
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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 | 0.000 |
| 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".