Hybrid Time-Switching and Power-Splitting EH Relaying for RIS-NOMA Downlink
Bibliographic record
Abstract
Reconfigurable intelligent surface (RIS) offers a potential performance boost for next-generation wireless networks, with significant improvements in spectral and energy efficiencies. In this paper, considering a two-user multiple-input single-output non-orthogonal multiple access (MISO-NOMA) downlink, we propose a RIS-aided cooperative transmission scheme using hybrid simultaneous wireless information and power transfer (SWIPT) and transmit antenna selection (TAS) protocols. Particularly, in the proposed scheme, the cell-center user servering as a relay helps the cell-edge user with the support of hybrid time-switching (TS), power-splitting (PS) SWIPT protocols and RIS passive beamforming. To evaluate the network performance, we first derive the best- and worst-case of closed-form expressions in terms of outage probability. For gleaning further insights, we also investigate the diversity order and the high signal-to-noise ratio (SNR) slope for the relevant outage probability. Simulation and numerical results demonstrate the achievable performance improvement for our proposed scheme with respect to schemes of orthogonal multiple access (OMA), non-cooperative NOMA and cooperative NOMA without RIS. In addition, the proposed scheme achieves a higher diversity gain with more transmit antennas and RIS elements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".