Enhancing LEO direct-to-satellite channel modeling with the shadowing effect via K-distribution
Why this work is in the frame
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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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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 | 0.001 | 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 it