Channel characterization and analysis for indoor non-line-of-sight ultraviolet communications
Bibliographic record
Abstract
Current research on ultraviolet (UV) channel modeling has predominantly emphasized outdoor environments, whereas indoor scenarios remain comparatively underexplored. To address this research disparity, we conduct a systematic investigation of UV propagation characteristics in indoor environments. First, we develop a non-line-of-sight channel model for obstruction-free indoor UV scenarios, where the received pulse energy incorporates contributions from both air scattering and multisurface reflections off walls, the ceiling, and the floor. Building upon this foundation, we extend the modeling framework to obstructed indoor UV scenarios by integrating spatial parameters of obstacles to achieve enhanced fidelity with practical indoor communication configurations. Simulation results demonstrate that the path loss curves obtained by the proposed model agree well with those obtained by the reflection-assisted Monte Carlo photon-tracking model. Moreover, we investigate the indoor UV path loss, scattered energy, and reflected energy dependencies on important variables, including communication range, transceiver elevation angles, receiver field-of-view angle, and obstacle parameters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".