Data-Oriented NOMA for Semi-Grant-Free Hybrid Satellite Terrestrial Networks
Bibliographic record
Abstract
Satellite networks have become central to the evolution of modern communications systems due to their potential for extensive global coverage. However, high latency in satellite systems poses critical challenges for delay-sensitive applications, underscoring the need for performance metrics that characterize ultra-reliable low-latency communications. To address these challenges, data-oriented approach, which is already widely studied in terrestrial networks, provides a fresh perspective by evaluating the transmission performance where it prioritizes both reliability and latency. This work introduces the data-oriented approach to uplink hybrid satellite-terrestrial networks (HSTNs), focusing on non-orthogonal multiple access (NOMA)-assisted semi-grant-free (SGF) transmission where terrestrial relays support the transmission between the satellite and users. Specifically, a novel grant-free user (GFU) admission protocol based on distributed contention control, the corresponding power allocation scheme for GFUs, and the relay selection procedure presented following the data-oriented approach. Then, the maximum number of GFUs that can be admitted under given data-oriented design requirements is determined. The analytical results are verified by extensive Monte-Carlo simulations, while a wide body of numerical results are also offered to understand and demonstrate the impacts of different system parameters. The proposed analytical framework offers useful insights toward the design of practical HSTNs, particularly in the context of delay-sensitive applications, by revealing the relationship between the amount of information data, the power consumption, and the satellite distance. The results demonstrate that the proposed scheme improves the DOR performance compared to conventional grant-free access and frequency division multiple access methods by dynamically selecting GFUs and allocating transmit power based on channel conditions. It is also shown that more GFUs can be admitted, especially with larger bandwidths and/or relaxed threshold settings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".