Aerial Reconfigurable Intelligent Surface-Assisted LPWANs for IoT: A Cross-Layer Analysis
Bibliographic record
Abstract
The utilization of low-power wide area network (LPWAN) technologies holds significant promise for numerous critical Internet of Things (IoT) applications that are characterized by low data rates and stringent power consumption requirements. However, LPWAN technologies face limitations, particularly in extending coverage in obstructed environments. Driven by the various abilities of reconfigurable intelligent surfaces (RISs) and the advanced features of unmanned aerial vehicles (UAVs), the integration of RIS into UAVs, which results in aerial RIS (ARIS), presents an attractive solution for enhancing LPWAN-based IoT connectivity. This letter introduces a novel solution to enhance the efficacy of LPWAN-based IoT by incorporating ARIS. A full cross-layer analysis is provided to illustrate the effectiveness of the solution. Furthermore, the impact of the UAV’s location and the number of reflective elements of the ARIS are examined in practical scenarios. The results illustrate that utilizing an ARIS significantly enhances the performance of the LPWAN-based system and results in a reliable long-range transmission operation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".