A matching approach for power efficient relay selection in full duplex D2D networks
Bibliographic record
Abstract
Full duplex relaying, which allows relays to transmit and receive signals simultaneously, can improve the spectrum efficiency and extend the range of device-to-device (D2D) communications. Due to the limited battery of mobile devices, it is essential to design a power-efficient relay selection scheme which can reduce the power consumption of devices and extend their lifetime. In this paper, we consider multiple D2D user pairs utilize full duplex relays to communicate using directional antennas. We formulate the power-efficient relay selection problem as a combinatorial optimization problem to minimize the power consumption of the mobile devices. Using a matching approach, we transform the problem into a one-to-one weighted bipartite matching problem. We then propose a power-efficient relay selection algorithm for relay-assisted D2D networks called PRS-D2D based on the Hungarian method to obtain the optimal solution in polynomial time. Simulation results show that our proposed algorithm improves the total power consumption of mobile devices by up to 32% comparing to an existing relay selection scheme in the literature.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".