IRSA Over Spreading Factors for Spatio-Temporal SIC in Scalable LoRaWAN IoT Networks
Bibliographic record
Abstract
The rapid growth of the Internet of Things (IoT) has triggered the need for scalable and energy-efficient communication solutions. While LoRaWAN is widely used for long-range wireless access, its Aloha-based MAC protocol struggles with high collision rates in dense networks. Existing solutions such as irregular repetition slotted ALOHA (IRSA) and contention resolution diversity slotted ALOHA (CRDSA) have improved network performance by using packet repetitions and successive interference cancellation. However, they do not fully leverage the unique properties of LoRaWAN Spreading Factors (SFs). To address this gap, we propose a new approach called SF-IRSA, where IoT devices transmit replicas using different SFs, enabling the decoder to apply an SF-IRSA-SIC process that leverages both temporal and spatial dimensions for efficient packet decoding. Our theoretical analysis and simulations show that SF-IRSA outperforms IRSA and CRDSA in terms of throughput and reliability. Specifically, using up to two SFs results in a 16.2% increase in the asymptotic throughput compared to standard IRSA. When extending to three SFs, the throughput gain reaches 116.9%, with a maximum of $\mathbf{2 2 4. 5 2 \%}$ while using $\mathbf{6}$ SFs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".