STAR-RIS Aided RSMA for Multi-Group Joint Multicast and Unicast Transmission
Bibliographic record
Abstract
Rate splitting multiple access (RSMA) is a novel transmission technique that enables simultaneous delivery of common and private messages to multiple users, which is suitable for joint multicast and unicast transmission. In this paper, a multi-group joint multicast and unicast downlink system is considered, where users are divided into different groups. A user grouping policy based on joint services and channel conditions is investigated, and a sum-rate maximization problem for the downlink RSMA system is formulated. Furthermore, the simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is introduced to improve the extremely poor user channel conditions. Then a joint optimization framework based on alternating optimization (AO) for user grouping, phase adjustment, and resource allocation in STAR-RIS-assisted RSMA system is proposed, where successive convex approximation (SCA) and the projected gradient method (PGM) serve as key optimization algorithms. Simulation results demonstrate that an appropriate user grouping policy fully exploits the performance of RSMA, and the introduction of STAR-RIS significantly further enhances the system capacity across various channel scenarios. Notably, when multiple users experience poor channel conditions, STAR-RIS effectively improves the channel conditions and incorporates them into RSMA groups, further boosting overall system performance.
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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 | 0.000 |
| 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".