Multi-Gateway LoRaWAN Throughput Modeling in Direct-to-Satellite IoT Constellations
Bibliographic record
Abstract
The emerging paradigm of Direct-to-Satellite Internet of Things (DtS-IoT) heralds a new era of global IoT connectivity unlocked by gateways in Low-Earth Orbit (LEO). Among the spectrum of technologies for achieving DtS-IoT, LoRaWAN, which relies on duty-cycled ALOHA channel access over unlicensed bands, emerges as a promising candidate. Lo-RaWAN's broad adoption in terrestrial IoT applications paves the way for a seamless Space-Terrestrial IoT integration. Lo-RaWAN distinctively allows multiple gateways to receive uplink packets simultaneously, an appealing feature for proliferated DtS-IoT constellations leveraging multiple satellites. However, existing theoretical throughput models for static multi-gateway LoRaWAN systems have not been evaluated in the more complex and dynamic satellite context. Our study addresses this gap by adapting, extending, and fine-tuning throughput models for the multi-gateway LEO DtS-IoT scenario. This approach will enable the rapid analysis of various LoRaWAN constellations to optimize their performance, addressing a critical need in current DtS-IoT mission design and operations. Additionally, we validate the proposed modeling with a comprehensive and realistic simulation campaign. Differences between the model predictions and simulation results remain below 5%. Results show that the proposed modeling is accurate and insightful, offering valuable projections into the performance of forthcoming LoRaWAN DtS-IoT constellations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".