Analysis and Exploitation of Diplexer-based Fully Passive Harmonic Transponder for 5G Applications
Bibliographic record
Abstract
An analytical model for the conversion loss (CL) calculation of diode-based fully passive harmonic transponders is proposed in this work. Given diode's SPICE parameters, the proposed model can accurately predict second-harmonic output based on RF input power. Thus, CL can be easily obtained. Such an accurate analytical model can be applied to evaluating potential CL performance of each diode candidate. Then, a novel diplexer-based fully passive harmonic transponder is designed, prototyped and measured. Through the introduction of the diplexer, traditional stringent antenna design restrictions can be alleviated. For an input power level of -30 dBm, measured CL of the proposed harmonic transponder is around 20.2 dB, which is more than 10 dB lower than the state-of-the-art counterpart.
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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 | 0.000 | 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".