A Novel Antenna Matching Technique for Joint Wireless Communication and Energy Harvesting
Bibliographic record
Abstract
Summary form only given. Merging the fields of digital communication, energy harvesting (EH), sensing, and RF system design is fundamental for achieving the best trade-off between the competing performance metrics. Matching the Low-Noise Amplifier (LNA) or the energy harvester input to the antenna source impedance is critical for maximizing the achievable rate or the power transfer efficiency. Therefore, this maximization needs to be optimized using joint performance metrics. In this paper, we introduce an optimized antenna matching technique for joint wireless communication and energy harvesting. The enhanced antenna matching process integrates the information theoretic capacity equation and Maxwell's equations. The resulting system substantially improves the trade-off between the harvested power and spectral efficiency based on our joint optimization approach. We design a 24 x 26 m2compact wideband elliptical planar antenna using an FR4 substrate of 1.6mm thickness and a 4.4 dielectric constant. We optimize a lossless common matching network that matches the designed antenna to a LNA and an EH Schottky diode. The common matching network constitutes an ideal transformer in parallel with an inductor. The designed wideband antenna operates between 5.5-8 GHz.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".