Robust Beamforming for Enhancing User Fairness in Multibeam Satellite Systems With NOMA
Bibliographic record
Abstract
This paper proposes a downlink transmission scheme exploiting robust beamforming in conjunction with non-orthogonal multiple access for multibeam satellite systems to enhance the spectral efficiency and user fairness. Specifically, by employing the imperfect channel state information and considering the fairness among multiple satellite terminals (STs), we first formulate an optimization problem to maximize the sum <inline-formula><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula>-fair utility, while guaranteeing the transmit power budget and quality-of-service requirement of each ST. Since the original problem is nonconvex, we then adopt a discretization method to convert the channel uncertainty into deterministic forms, and propose an iterative penalty function algorithm combined with sequential convex approximation to obtain the optimal solution. Finally, simulation results are given to confirm the effectiveness and superiority of the proposed scheme over some existing works. It is also shown that our scheme can achieve a good balance between the system performance and user fairness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".