Partial Non-Orthogonal Multiple Access: A New Perspective for RIS-Aided Downlink
Why this work is in the frame
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Bibliographic record
Abstract
In this letter, a novel reconfigurable intelligent surface (RIS) aided partial non-orthogonal multiple access (P-NOMA) downlink is investigated, where the passive beamforming on the RIS is designed for serving paired user equipments (UEs), with their signal frequencies partially overlapped. We consider the general case that all UEs have both direct and reflection links undergoing Rayleigh and Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> fadings. By approximating the effective channel gains of the P-NOMA UEs as Gamma random variables, we derive the closed-form expressions of the lower and upper bounds on the achievable rates. The accuracy of the derived close-form expressions are verified through simulation and numerical results, where the proposed RIS-aided P-NOMA downlink considerably outperforms the conventional NOMA and orthogonal counterparts.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.001 |
| 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 it