Sum Rate Maximization for RIS-Aided NOMA With Direct Links
Bibliographic record
Abstract
Reconfigurable intelligent surface (RIS) is an electromagnetic surface, and has abundant low-power reflecting elements which can dynamically tune the wireless propagation environment by changing their phase shifts. Non-orthogonal multiple access (NOMA) technology would be capable of greatly improving the spectral efficiency of communication via differentiating users through power. Via integrating RIS into NOMA to improve wireless system performance, we take an uplink RIS-aided NOMA network with direct links into account, where multiple users communicate with the access point under the assistance of a RIS with multiple elements. We aim to obtain the maximum sum rate by optimizing the phase matrix of RIS subject to users’ transmitted power. Two algorithms are put forward to conquer the formulated intricate non-convex puzzle. More exactly, the semidefinite programming is proposed to relax the function while the majorization-minimization is used to derive the closed-form phase shifts. Finally, presented simulation results demonstrate the high performance of proposed schemes compared with no RIS and show the benefits of the existence of direct links.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".