Distance Distributions and Coverage Probabilities in Poisson-Delaunay Triangular Cells With Application to Coordinated Multipoint Wireless Power Transfer
Bibliographic record
Abstract
This paper investigates the power coverage probability in cooperative wireless powered communication networks, where multiple access points collaborate to meet the energy demands of low-power devices. Based on the theory of Poisson-Delaunay triangulation, the probability density functions (PDF) of the Euclidean distance between the access points of the Poisson-Delaunay triangular cell and typical devices are derived. By using the theory of stochastic geometry and the obtained PDFs, the closed-form expressions of the wireless power coverage probability are obtained for three typical locations of the devices. As the wireless power coverage probability expressions involve the extended generalized multivariate MeijerG function (EGMMGF), a new implementation enabling numerical calculation of the EGMMGF is also proposed. The impacts of the main network parameters on the performance of the proposed framework are analyzed. In particular, comparative results show the significant gains that can be achieved in the wireless power coverage when multiple access points participate in the wireless power transfer or when the density of the network’s access points is increased, as compared to the non-cooperative scheme.
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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 | 0.010 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".