Energy management scheme for wireless powered D2D users with NOMA underlaying full duplex UAV
Bibliographic record
Abstract
Device-to-Device (D2D) communications underlaying Unmanned aerial vehicle (UAV) with its mobility extend the coverage and improve the data rate. In this paper, we propose an energy management scheme for wireless powered D2D users with NOMA underlaying full-duplex (FD) UAV. Here, the cellular transmitters (CTs) and D2D transmitters (DDTs) first harvest energy from the radio frequency (RF) signals of the UAV. Then, the CT communicates with the cellular receivers (CRs) using the FD-UAV as a relay. On the other hand, DDT communicates with its two D2D receivers (DDRs) using the NOMA. We formulate the problem as a mixed-integer non-linear programming (MINLP) form and then divide it into two sub-problems. In the first sub-problem, an optimal value of time allocation for energy harvesting (EH) for DMG is estimated, whereas, in the second subproblem, the power of DDT in each DMG is optimized using the variable changing technique. Finally, the joint time allocation and power control scheme is proposed to achieve the maximum energy-efficiency (EE). Numerical results demonstrated that the proposed scheme achieves better results as compared to the existing conventional NOMA and orthogonal multiple access (OMA) schemes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".