Outage Probability Analysis of LR-FHSS in Satellite IoT Networks
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Bibliographic record
Abstract
Long-range frequency-hopping spread spectrum (LR-FHSS) is a promising solution for long-range and dense deployment of Internet of Things (IoT) networks since it can provide a significant capacity improvement compared to the conventional Aloha-based chirp spread spectrum (CSS). In this letter, we present an analytical approach for deriving the outage probability of LR-FHSS in a satellite-based IoT network taking into account the noise, channel fading impairments, and more importantly, the capture effect. The obtained analytical expressions are validated with computer simulations and show that for a typical target outage probability of 10−2, exploiting LR-FHSS in the considered system model can serve up to 60, 000 and 120, 000 end devices per hour for 48 bytes of information using two specified data rates in the North America region. These numbers present significant capacity increases over the conventional low-power long-range (LoRa) network.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it