Competitive IRS Assignment for IRS-Based NOMA System
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".