Competitive IRS Assignment for IRS-Based NOMA System
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Bibliographic record
Abstract
This letter considers the downlink transmission of an intelligent reflecting surface (IRS)-aided multi-carrier (MC) non-orthogonal multiple access (NOMA) system, referred to as the IRS-aided MC-NOMA system. Due to the limitations on the availability of the IRS, a limited number of channels can be served with the support of the available IRS units. Therefore, a competitive approach is proposed to assign the available IRS units for the intended channels, and to group the users in each channel (i.e., clustering). To validate the effectiveness of the proposed competitive approaches, a power minimization problem is considered that aims to minimize the total transmit power while ensuring a set of quality-of-service requirements. Because of the non-convex nature of the joint power optimization problem, we develop a simple sequential convex approximation algorithm to solve it. Simulation results demonstrate that the IRS-aided MC-NOMA system with proposed IRS-assignment and grouping approaches outperforms the random IRS-assignment and grouping approaches regarding the transmit power consumption.
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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 | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it