On the Effect of Multi-Packet Reception on Redundant Gateways in LoRAWANs
Bibliographic record
Abstract
In this paper, the impact of redundant packet reception at multiple gateways on data reliability is studied under the LoRaWAN architecture. Given a successful transmission could be the result of either a first attempt after a data packet's generation, or a retry after several transmission failures, the Average Successful Transmission Probability (ASTP) is introduced to qualify LoRaWAN's reliability performance. To calculate the probability of a successful reception without retransmission, we consider all the possible causes for a packet collision. The number of potential interferers, which is vital for the collision analyses and directly determined by the relative locations of the relevant multiple gateways, is determined by geometric arguments. Similarly, the probability for achieving a successful retransmission is also obtained. Finally, ASTP is rigorously modeled as a function of end device density, gateway density, and traffic intensity. The analytical results have been verified by extensive simulation experiments. We believe that the analytical model can provide useful insights into the scalability of LoRaWANs and provide guidelines for their deployments.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".