Link-Level Performance Analysis of DVB Standards in Ultra-Dense LEO Satellite-Terrestrial Networks
Bibliographic record
Abstract
Ultra-dense low earth orbit (LEO) satellite terrestrial networks (ULSNs) are considered as a crucial component of future six generation (6G) networks, offering ubiquitous and massive services for various applications. However, for the development of advanced physical layer technologies for ULSNs, a comprehensive link-level simulation tool that integrates up-to-date satellite communication protocols becomes paramount and is urgently needed. In this paper, we develop a versatile simulator for the link-level performance analysis of ULSNs under the prevalent digital video broadcasting (DVB) standards. We first establish a complete satellite-terrestrial microwave channel model, taking practical factors such as rain attenuation, cloud attenuation, and Doppler frequency shift into consideration. Subsequently, the whole physical layer modules tailored for satellite-terrestrial microwave communication are implemented, including diverse physical layer modulation and coding schemes (MCSs). Furthermore, we realize adaptive coding and modulation (ACM) for adaptive channel performance simulation. Finally, comparative performance analysis using the established channel model is conducted to demonstrate the effectiveness of different MCSs of DVB standards. The complete link-level performance analysis based on our self-developed simulator can advance the field of satellite-terrestrial microwave communication and provide valuable insights for further exploration of ULSNs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".