Enabling Broadband Internet Access in Remote and Rural Communities Using HAP-Based Multi-Hop FSO/RF Transmissions
Bibliographic record
Abstract
In this article, we propose a reliable high-altitude platform (HAP)-based end-to-end (E2E) solution for broadband Internet access in remote and rural communities. In particular, we adopt parallel free-space optical (FSO)/radio frequency (RF) transmission for the feeder and access links to explore their complementary properties, while FSO transmission is only used for inter-HAP links as there are less turbulence effects. Using FSO links, the proposed solution supports high-data-rate transmissions to access points (AP) in hotspot regions with high-er demand for Internet connectivity. To maintain wide-area coverage, the RF link serves dispersed users regardless of location. To increase the FSO link's usability and minimize the probability that the system resorts to RF transmission, we introduce backup HAPs for feeder and access links. The system will switch between primary and backup HAPs depending on the channel quality, which helps considerably to improve system performance. The proposed E2E solution can achieve a spectral efficiency of 8.5 bps/Hz for APs and 4 bps/Hz for remote users outside the coverage of APs under a typical operating scenario.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".