Uplink Transmission Policies for LoRa-Based Direct-to-Satellite IoT
Why this work is in the frame
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Bibliographic record
Abstract
Direct-to-Satellite IoT (DtS-IoT) is a promising approach to deliver data transfer services to IoT devices in remote areas where deploying terrestrial infrastructure is not appealing or feasible. In this context, low-Earth orbit (LEO) satellites can serve as passing-by IoT gateways to which devices can offload buffered data to. However, transmission distances and channel dynamics, combined with highly constrained devices on the ground makes of DtS-IoT a very challenging problem. Here, we present LoRa-based approaches to realize scalable and energy-efficient DtS-IoT. Our study includes the Long Range-Frequency Hopping Spread Spectrum (LR-FHSS) physical layer, currently on the roadmap of future space IoT projects. Specifically, we propose uplink transmission policies that exploit satellite trajectory information. These schemes are framed with a theoretical Mixed Integer Linear Programming (MILP) model providing an upper bound on performance as well as inspiration for scheduled DtS-IoT solutions. Simulation results provide compelling evidence that trajectory based policies can duplicate the amount of IoT nodes, while specific variants can further boost the scalability by 30% without incurring energy penalties. We also quantify that LR-FHSS can improve the deployment scalability by a factor of 75x at the expenses of 30% higher device’s power consumption compared to the legacy LoRa modulation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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