Two-Step Adaptive Grouping Access Based on RSMA for Multibeam Satellite System
Bibliographic record
Abstract
Multibeam satellite system (MSS) plays an increasingly important role in the future communication system because of the ability to provide seamless information services. However, multibeam technology will cause serious inter-beam co-frequency interference (IBCFI), significantly deteriorating communication performance. Existing IBCFI management schemes mainly depend on precoding technologies, which regard MSS as multi-antenna systems and ignore characteristics of satellite beam gain and the limited computational resources. Meanwhile, terrestrial channels tend to be independent while satellite channels have a high correlation, which is rarely considered by existing work. On the other hand, rate-splitting multiple access (RSMA) has recently emerged due to the advantages of flexible multiple access and robust interference management. Therefore, we design a two-step adaptive grouping access scheme based on the promising RSMA to handle these challenges, where the first step takes the characteristics of satellite beam gain and computational resources into consideration, and the second step optimizes the channel correlation. Building on the two-step adaptive grouping access scheme, we formulate different weighted sum rate (WSR) maximization problems for different user groups. Furthermore, we introduce an improved alternating optimization algorithm to solve these non-convex problems. Finally, simulation results verify the effectiveness of our proposed scheme in WSR and computational 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".