Subgrouping‐based multicast transmission over high‐throughput satellite systems with beam cooperation
Bibliographic record
Abstract
Summary This paper investigates the multicast transmission for multicast services in high‐throughput satellite (HTS) systems. Considering the multibeam multicast feature of HTSs, cooperative transmission among beams is involved in to improve the efficiency of the multicast transmission. Since the multicast transmission rate depends on the worst user channel state, all the users experience an unreasonably low rate. In this situation, subgrouping techniques are employed to increase transmission rates of users. A subgrouping‐based multicast transmission problem aiming at maximizing the lowest transmission rate of the users is studied to guarantee fairness among users. We formulate the problem as a max–min optimization problem and propose two low‐complex subgrouping algorithms for this problem. Additionally, we also consider multicasting in a single beam and devise a two‐layer transmission scheme for it. In the performance evaluation part, besides the impact of parameters on subgrouping performance, we analyze the performance and the computational complexity of the proposed algorithms. The results indicate that the two subgrouping algorithms can achieve favorable performance with low complexity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".