Multi-Gateway LoRaWAN Throughput Modeling in Direct-to-Satellite IoT Constellations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div> 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. </div>
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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