Diplexer-Based Fully Passive Harmonic Transponder for Sub-6-GHz 5G-Compatible IoT Applications
Bibliographic record
Abstract
A novel diplexer-based fully passive transponder is presented in this paper, which targets sub-6-GHz 5G-compatible internet-of-things applications. To alleviate the antenna design restrictions of traditional transponder with two separate antennas, a new architecture has been proposed with the introduction of a diplexer, which allows transponder to simply employ a dual-band antenna. In this paper, a dual-band circularly polarized omnidirectional spiral slot antenna, with enhanced bandwidth and gain performance, is designed as the transponder Tx/Rx antenna. Besides the new architecture, a diode selection criterion is proposed as well. Analytical models are derived, showing relationships between the diode's SPICE parameters and the conversion efficiency or conversion loss (CL) of such diode-based transponders. With help of the analysis, transponder designers can easily identify diodes to implement transponders with better performance. Under the guidance of the criterion, low-barrier diode SMS7630 is chosen for verification. Measured CL results of the transponder circuitry part show a noticeable improvement over the state-of-the-art works. The complete prototype was tested with radar's transmitting power of 25 dBm, and it presents a maximum read-out distance up to 7 m when the operating fundamental frequency is 3.5 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".