On Optimal Power Allocation in Multibeam Multicast NOMA for Satellite Communication Systems
Bibliographic record
Abstract
This paper addresses two key challenges in the multibeam multicast non-orthogonal multiple access (MB-MC-NOMA) scheme and respective beamforming design problems in satellite systems. Achieving the max-min fairness (MMF) and maximum sum-rate among multiple multicast groups of users are jointly considered in a theoretical information framework. It is assumed that each frame contains information of multiple users in multicast transmission. Therefore, contrary to the unicast linear precoding, we have developed the multicast linear precoding with mapping function considering trade-offs to deal with the lack of the spatial degree of freedom. In our proposed scheme, each beamforming vector conveys information to more than one group of users in a NOMA framework, relying on the superposition techniques at the transmitters and successive interference cancellation (SIC) at the receivers. We have derived the capacity rates achievable in each beam, proposing the methods to maximize the minimum rate and weighted sum-rate. Considering the dependency of the broadcasting power and the respective achievable rates, the equivalent channel and water-filling algorithm for the MB-MC-NOMA is developed; as such, the optimal transmit power density for the groups of users within multiple beams are efficiently computed. The extensive simulation results confirm the proposed theoretical findings, providing a considerable boost in both minimum-rate and sum-rate with respect to state-of-the-art multibeam multicast satellite systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".