Power Optimization in RIS-Assisted P-NOMA for Full-Duplex 6G Vehicular Networks
Bibliographic record
Abstract
In this paper, we propose a new full-duplex transmission reconfigurable-intelligence-surface (RIS)-assisted vehicular communication framework aimed at enhancing vehicular communications among connected vehicles under congested urban areas. By integrating power-domain non-orthogonal multiple access (P-NOMA) and enabling spectrum reuse between the RIS-assisted vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications, the proposed framework facilitates concurrent data transmission to multiple vehicles via a single sub-channel, effectively enhancing data rate performance without requiring additional bandwidth. To further improve the spectrum efficiency, we incorporate NOMA as a cross-tier interference coordination mechanism and employ successive interference cancellation (SIC) for mitigating interference originating from V2V communication, rendering our solution highly suitable for vehicular applications. We derive optimal values for transmit power and NOMA pair coefficients, and employ a successive convex approximation (SCA) technique to optimize RIS phase shifts. The superiority of the proposed approach is evaluated in comparison to the dominant interference scenario and orthogonal multiple access (OMA) technique with respect to the data rate.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".