A Fuzzy Method for Joint Resource Allocation and Stable Pairing in D2D Communications
Bibliographic record
Abstract
In this paper, a device-to-device (D2D) communications multiple-metric scenario for resource allocation and pairing is studied. To improve the D2D connection stability, a fuzzy method based on the data rate and battery levels of potential D2D pairs is proposed in such a way that the set of D2D transmitters is considered as a fuzzy set. A single cell scenario with a cellular user and some co-channel D2D nodes consists of D2D receivers, and transmitters are taken into account. Furthermore, a stable fuzzy pairing criterion is proposed for the selection of the best D2D transmitter. The proposed method is compared with three other pairing methods named the maximum sum rate, constant pairing method, and random pairing method. The simulation results show that the mentioned pairing method outperforms the other three methods in terms of stability and fairness criteria and follows the maximum sum-rate method from the sum-rate criteria point of view.
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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 | 0.001 | 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 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".