Fair Scheduling of Wireless Power Transfer to Nonlinear Energy Harvesters
Bibliographic record
Abstract
The performances of two wireless power transfer (WPT) scheduling schemes,time sharing(TS) andspatial multiplexing(SM), in terms of provisioning fairness among energy receivers (ERs), are studied and compared while taking into account the nonlinearity of the harvesting circuits. In the network, the multiple-antenna energy transmitter attempts to maximize the harvested energy by the ER which has accumulated the minimum amount of energy among all single-antenna ERs during the WPT block—hence themax-minfairness criterion. Two network scenarios are studied: homogeneous, where similar channel coefficients are assumed for all the well-apart ERs, and heterogeneous, where the said coefficients can take arbitrary values. For WPT in single-band or multi-band, we analytically prove that the optimal scheduling policy in the homogeneous scenario is to allocate each ER thefull transmit powerwith uniform distribution of charging times among ERs, rather than to allocate thefull power transfer blockwith uniform distribution of the power among ERs. Generalization of the network to the heterogeneous scenario aims to find the optimal beamforming vectors for the SM scheme and the optimal time sharing vector for the TS scheme. We form the max-min fairness optimization problems for both scheduling techniques, in single- and multi-band WPT scenarios. The problems, which are nonlinear and non-convex, are solved through exhaustive search algorithms. It is proven that the TS scheduling outperforms the SM one in terms of max-min fairness as a result of taking into account the inherent non-linearity of the harvesting devices.
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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 | 0.003 | 0.006 |
| 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".