Single-Collision Model for NLoS UV Channels: Joint Scattering and Reflection Effects
Bibliographic record
Abstract
Ultraviolet (UV) communication research has prioritized channel modeling for its critical role in system optimization. Current non-line-of-sight (NLoS) UV modeling mainly addresses obstacle-free scenarios and single-obstacle situations: the former manifests constrained applicability at small transceiver elevation angles with obstacle susceptibility, while the latter suffers from high modeling complexity and can only handle one-obstacle scenarios, which pose critical challenges for Internet of Things applications. To overcome these limitations, we propose a single-collision model for short-range NLoS UV channels incorporating both scattering and reflection effects. Initially, the impact of air scattering on the received pulse energy is presented for diverse obstacle situations, where an obstacle-boundary approximation method (OBAM) is developed to reduce the modeling complexity. Besides, the dimensions, coordinates, shapes, orientation angles, and number of obstacles are considered to emulate practical environments. Subsequently, the impact of obstacle reflection on the received pulse energy is investigated for single, double, and multiple obstacle situations. On this basis, we account for certain scenarios where obstacle surfaces comprise multiple sub-regions, each characterized by distinct reflection coefficients attributed to their varying material compositions. Moreover, we verify the proposed model by comparing it with the Monte-Carlo photon-tracing (MCPT) model and the obstacle-free integral model via simulations. These results demonstrate that the path loss curves obtained by the proposed model exhibit close alignment with those simulated by the MCPT model, while its calculation time is less than 10% that of the MCPT model. Additionally, when obstacle reflection is prominent, the assessment error of the proposed OBAM can be ignored in estimating the path loss of NLoS UV channels containing obstacles.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".