Interference issues in LoRaWAN: A comparative study using simulator and analytical model
Why this work is in the frame
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Bibliographic record
Abstract
Packet transmission in LoRaWAN implements the use of the unslotted ALOHA protocol, which leads to a high probability of interference, and thus, degrades the system performance. Reliability and scalability are affected by interference too. Different forms of studies, including experimental, analytical and simulation models, have been carried to find out how to minimize the effects of interference and maximize network performance. In this paper, we study the effects of interference in LoRaWAN by studying a number of important parameters in the comparison of an analytical model and a simulator. We have developed the analytical model by extending one model from the literature, developing in the process an extended theoretical framework to improve the original model. Also, a popular simulator has been selected from different alternatives. Several modifications were made to both the analytical model and the simulator to make them flexible enough to provide a common platform for the comparative analysis. This study provides some important perspectives on the trade-offs of various parameters in terms of uplink delivery rate and enables choosing the optimum configuration in terms of packet generation interval, coding rate, spreading factor taking into account the number of nodes in the system. Thus, it enables us to find out similarities and inconsistencies between analytical model and simulations.
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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