Enhancing LEO direct-to-satellite channel modeling with the shadowing effect via K-distribution
Bibliographic record
Abstract
Accurate channel modeling is crucial for optimizing direct-to-satellite Internet of things (DtS-IoT) communications via low Earth orbit (LEO) nanosatellites. Traditional channel models for land-mobile satellite systems often overlook the significant impact of shadowing at low elevation angles, limiting their applicability to DtS-IoT scenarios. This paper presents an enhanced finite-state Markov channel with two-sectors (FSMC-TS) model that integrates shadowing effects into the bad sector (B-Sector) by using the K-distribution for modeling. This enhancement captures the combined effects of multipath fading and shadowing, providing a more accurate representation of the channel conditions experienced in DtS-IoT applications. Simulation results show that the enhanced model aligns closely with analytical bit error rate (BER) predictions, particularly at higher signal-to-noise ratios (SNRs), with less than 1% deviation from theoretical values. The Enhanced FSMC-TS model offers a valuable tool for reliable DtS-IoT communication systems, addressing a critical gap in existing channel modeling approaches.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".