Energy management scheme for wireless powered D2D users with NOMA underlaying full duplex UAV
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it