Harvested Power Fairness-Based Multi-Carrier NOMA IoT Networks With SWIPT
Bibliographic record
Abstract
The integration of simultaneous wireless information and power transfer (SWIPT) in multi-carrier non-orthogonal multiple access (NOMA) systems has been recently investigated as a potential paradigm to improve the lifespan of energy-constrained Internet-of-Things networks. In this letter, we develop a fairness-aware design for a multi-carrier NOMA SWIPT-based system that attempts to maximize the minimum harvested power subject to quality-of-service constraints. With this design, each user’s power allocation, and the power splitting ratio are jointly computed by solving the corresponding optimization problem. To deal with the non-convexity nature of the formulated max-min optimization problem, an iterative mechanism with a second-order cone method is proposed. Simulation results indicate that our design maintains a better user fairness with respect to the harvested power.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".