Distributed simultaneous wireless information and power transfer in multiuser amplify‐and‐forward ad hoc wireless networks
Bibliographic record
Abstract
Summary This paper studies the problem of stable node matching for distributed simultaneous wireless information and power transfer in multiuser amplify‐and‐forward ad hoc wireless networks. Particularly, each source node aims to be paired with another node that takes the role of an amplify‐and‐forward relay to forward its signal to the destination, such that the achievable rate is improved, in return of some payment made to the relaying node. Each relaying node splits its received signal from its respective source into two parts: one for information processing and the other for energy harvesting. In turn, a matching‐theoretic solution based on the one‐to‐one stable marriage matching game is studied, and a distributed polynomial‐time complexity algorithm is proposed to pair each source node with its best potential relaying node based on the power‐splitting ratios, such that their utilities or payments are maximized while achieving network stability. For comparison purposes, an algorithm to enumerate all possible stable matchings is also devised to study the impact of different matchings on the source and relay utilities. Simulation results are presented to validate the proposed matching algorithm and illustrate that it yields sum‐utility and sum‐payment that are closely comparable to those of centralized power allocation and node pairing, with the added merits of low complexity, truth telling, and network stability.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".