Impulsive noise modeling with stable distributions in fading environments
Bibliographic record
Abstract
The paper introduces a statistical and physically based mechanism giving rise to /spl alpha/-stable noise models. We show that the additive interference which is present in many environments can be modeled as symmetric /spl alpha/-stable by assuming: (i) independent signaling (effects) from a large number of interferers of the same type (modulation); (ii) Poisson distribution of interferers in space; and (iii) inverse power attenuation of the signal strength with distance. Our approach to /spl alpha/-stable noise modeling is based on the LePage series representation as opposed to the influence function approaches. The formulas derived are used to predict noise statistics in environments with lognormal shadowing and Rayleigh fading. The LePage series framework allows us to investigate practical constraints in the system model adopted, such as the finite number of interferers and the nonhomogeneous Poisson fields of the interferers. We characterize the interference for multiple access communication systems in which the interferers are assumed to be Poisson-distributed in the plane.
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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".