Energy Harvesting Aided PASTAR-RIS-Empowered NOMA System in the Presence of Impulsive Noise
Bibliographic record
Abstract
Reconfigurable intelligent surfaces are considered as a promising emerging technology for beyond-$5^{\text{th}}$Generation /$6^{\text{th}}$Generation wireless systems. Leveraging energy harvesting technology, this paper considers an energy constrained source node that harvests energy from a power source through passive reconfigurable intelligent surfaces. The harvested energy is then utilized to serve multiple downlink power domain non-orthogonal multiple access users with the aid of partitioned active simultaneous transmitting and reflecting reconfigurable intelligent surface. Partitioned active simultaneous transmitting and reflecting reconfigurable intelligent surface as well as power domain non-orthogonal multiple access users are assumed to be impaired by impulsive noise. Hardware impairments are also accounted for at the source node and the power domain non-orthogonal multiple access users. It is shown that the impact of impulsive noise is negligible for imperfect successive interference cancellation at the power domain non-orthogonal multiple access users. However, for perfect successive interference cancellation, impulsive noise at the partitioned active simultaneous transmitting and reflecting reconfigurable intelligent surface degrades performance more severely for the nearest user than the farthest.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".