Precoding and Scheduling in Multibeam Multicast NOMA based Satellite Communication Systems
Bibliographic record
Abstract
In this paper, we propose a multibeam multicast non-orthogonal multiple access-based (MB-MC-NOMA) scheme for satellite communication systems. Building upon the multi-beam transmission, we exploit precoding and NOMA techniques to deal with inter-and intra-beam interference, respectively. Combining these two techniques in the multicast transmission is not trivial as both scheduling, and the precoding design shall be reconsidered. Indeed, it is shown there is a contradiction between criteria for efficient user scheduling in MC-NOMA and MC-linear precoding: while the first one requires a strong channel gain imbalance between user terminals, the latter seeks user channel vector similarities. To tackle this problem, we propose a user scheduling in MB-MC-NOMA, which provides a good balance between these two criteria. Besides, the MC-linear pre-coding is designed with aid of a mapping function that deals with the lack of spatial degrees of freedom. The numerical simulation results show that the performance of the MB-MC-NOMA scheme is improved by using the proposed user scheduling and a novel precoding design based on the singular value decomposition (SVD). Moreover, the MB-MCNOMA scheme outperforms its orthogonal version scheme. In particular, we observe that the throughput of the MB-MC-NOMA is increased a 25% with respect to MB-MC-OMA scheme under certain fair conditions.
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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 | 0.000 | 0.000 |
| 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".