Advancements on Packet Scheduling in Hybrid Satellite-Terrestrial Networks
Bibliographic record
Abstract
The past years have seen an explosion in the number of broadcasting network standards and a variety of multimedia services available to the mobile mass-market. Satellite communications has been gaining phenomenal growth and increasing interest over the last decade in its complementary but essential role for offering seamless broadband service coverage to potential users at every inch of the earth’s surface. However, mobile satellite network often feature unidirectional and long-latency, a great deal of research effort has been attempted for this bottleneck. Given the absence of feasible power control mechanism and reliable feedback information, the role of packet scheduling in such a network with large delay-bandwidth product is extremely challenging. In fact, an optimized media access control (MAC) layer protocol is essential for cost-efficient satellite networks to compete with other terrestrial modalities. In particular, the integration and convergence between satellite network and conventional terrestrial backbone infrastructure offers promising solutions for next generation service provisioning. In this chapter, the authors give a survey on the state-of-the-art on packet scheduling in hybrid satellite-terrestrial networks (HSTN). A whole range of issues, from standardization, system to representative scheduling methodologies as well as their performance trade-offs have been envisioned. Moreover, the authors investigate viable solutions for effectively utilizing the limited/delayed feedbacks in resource management functions. They examine the flexibility and scalability for the alternative schemes proposed in this context, and analyze the performance gain achievable on essential QoS metrics, channel utilization, as well as fairness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".