LoRa-Empowered Multi-User Communication for IoT Wireless Networks
Bibliographic record
Abstract
<p>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.</p>
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How this classification was reachedexpand
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 | 0.001 | 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 teacher head, 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".