LoRa-Empowered Multi-User Communication for IoT Wireless Networks
Bibliographic record
Abstract
The emergence of Internet-of-Things (IoT) has led to the development of new energy efficient and long-range wireless technologies, which are necessary for the successful realization of IoT applications. Within this context, long range (LoRa) has emerged as one of the prominent low power wide area network (LPWAN) technologies that is envisioned to accommodate the future IoT requirements. However, current LoRa frameworks suffer from limited network capacity, particularly in dense deployments. Consequently, power domain-superposition modulation (PD-SPM) is integrated with LoRa to alleviate the aforementioned problem. In this paper, the error rate performance of a LoRa-enabled PD-SPM system is investigated over Rayleigh fading channels. Specifically, symbol error rate (SER) expressions are derived to characterize the performance of an arbitrary number of users under extreme fading conditions. Furthermore, Monte Carlo simulations are presented to validate the analytical framework.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".