Enhanced Resource Allocation for Beam-Hopping Satellite Networks with Rate-Splitting Multiple Access
Bibliographic record
Abstract
Low Earth Orbit (LEO) satellite systems provide geographically unrestricted services to ground users. However, the conflict between existing resource allocation schemes and the variability of inter-beam traffic is becoming increasingly prominent. To address this issue, this paper proposes a resource allocation strategy for beam-hopping satellite networks based on Rate-Splitting Multiple Access (RSMA) technology, aiming to reduce co-channel interference while improving system resource utilization. First, by analyzing the resource allocation challenges faced by beam-hopping satellite networks, including low spectrum utilization and co-channel interference, the background and motivation for the proposed strategy are provided. Next, RSMA technology is introduced, dividing user messages into common and private parts, and a corresponding resource allocation algorithm is designed to enhance spectrum utilization and reduce co-channel interference. Through the construction of a system model and simulation experiments, the effectiveness and performance advantages of the proposed strategy are verified. This study provides new ideas and methods for resource allocation in beam-hopping satellite networks, which is significant for improving system performance and service quality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".