Performance bounds evaluation of FH SS radio networks with interference modeled as a mixture of Gaussian and alpha-stable noise
Bibliographic record
Abstract
We consider the performance of frequency-hopping spread spectrum (FH SS) radio networks in a Poisson field of interfering terminals using the same modulation and power. The problem is relevant to mobile communication systems where the mobility of users requires random modeling of transmitter positions in the network. Assuming logarithmic attenuation of the signal strength over distance between the transmitter and receiver, we show that the interference in the network could be modeled as a mixture of Gaussian and circularly symmetric /spl alpha/-stable noise. Based on this model, we derive union bound approximations for the probability of error for FH systems with M-ary frequency shift keying (FSK). We generalize some of the results of Sousa (see IEEE Trans. I.T., vol.38, no.6, p.1743, 1992), where the focus was limited to Cauchy random variables (RVs), a special subclass of stable distributions, and where the effect of a background (Gaussian) noise was neglected. Numerical calculations and Monte Carlo simulations confirm the accuracy of our analysis. The results obtained allow the prediction of wireless system performance in environments varying from urban settings to office buildings.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".