Next-Gen Satellite System: Integrative Non-Orthogonal Broadcast and Unicast Services Based on Innovative Frequency Reuse Patterns
Bibliographic record
Abstract
The multibeam satellite system is crucial for providing seamless and various information services, such as broadcast and unicast messages. However, catering to the burgeoning number of users within a limited spectrum of resources presents formidable challenges. Therefore, we devise the non-orthogonal broadcast and unicast (NOBU) joint transmission framework using rate-splitting multiple access (RSMA), which leverages non-orthogonal transmission and precoding strategies. Furthermore, amalgamating traditional precoding with frequency reuse techniques, we propose two novel distributed frequency reuse (DFR) and centralized frequency reuse (CFR) strategies. Taking satellite beam gain characteristics and interference tolerance threshold into consideration, we further propose another two expansions of DFR and CFR strategies with innovative inner and outer divisions. For the NOBU joint transmission based on four novel frequency reuse patterns, we maximize the weighted sum rate (WSR). Subsequently, we introduce an improved alternating optimization algorithm, adept at converting intricate non-convex problems into tractable convex counterparts. Simulation outcomes demonstrate that our proposed schemes have significant improvements in WSR performance and are promising for various practical applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".