Outage Probability Analysis of LR-FHSS in Satellite IoT Networks
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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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".