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.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".