Resource Allocation for Energy Harvesting D2D Communications Underlaying NOMA Cellular Networks
Bibliographic record
Abstract
This paper studies a resource allocation problem utilizing the power splitting (PS) architecture in SWIPT (Simultaneous Wireless Information and Power Transfer)- enabled device-to-device (D2D) communications underlaying a non-orthogonal multiple access (NOMA) cellular network, where the cellular users receive signal from the base station at different power levels. The device transmitter and receiver employ a PS architecture; hence a fraction of the received signal power is used for energy harvesting and the rest is used for information decoding. The device transmitter communicates with the device receiver such that the minimum rate requirement of the cellular users in the network is guaranteed. The formulated resource allocation problem, which aims at maximizing the throughput of D2D communications, is non- convex. Analysis and simplifications lead to an optimal solution obtained using the gradient descent method. The numerical results depict that the NOMA cellular network offers considerable D2D throughput gain over the benchmark orthogonal multiple access network. The results are significant for the adoption of energy harvesting D2D communications combined with NOMA-based cellular networks in the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".