Weighted Sum Rate Maximization for RIS Backscatter Aided NOMA Networks
Bibliographic record
Abstract
This paper proposes to integrate reconfigurable intelligent surface with backscatter communication (RIS-BackCom) for downlink non-orthogonal multiple access (NOMA) networks, where a RIS serves as a backscatter device to transmit the modulated signals to multiple single-antenna target users. Building upon the established system architecture, the weighted sum rate (WSR) is maximized for all the users under the constraints of total transmit power, RIS phase shift, rate fairness, and successive interference cancellation decoding rate. By employing the techniques of Lagrangian dual transform, quadratic transform and alternative optimization strategies, the original optimization problem is decomposed into three tractable sub-problems. Then, these sub-problems are effectively addressed using successive convex approximation and semidefinite relaxation methodologies. Experimental results demonstrate the feasibility and superiority of the proposed RIS-BackCom aided NOMA system.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".