A 2.4 GHz and 5.8 GHz tunable low-noise amplifier using PIN diode
Bibliographic record
Abstract
In this paper, a tunable low-noise amplifier (LNA) is developed. The operation bands of the LNA can be switched between 2.4 GHz and 5.8 GHz by controlling a PIN diode in the output matching network. At the forward bias state of the PIN diode, the LNA operates at 2.4 GHz, the measured gain is 16 dB and the noise figure is below 0.6 dB within a bandwidth of 100 MHz. At the reverse bias state of the PIN diode, the LNA works at 5.8 GHz, the measured gain is 9.7 dB and the noise figure is below 1.7 dB within a bandwidth of 200 MHz. Compared with the wideband LNA covering 2.4GHz and 5.8GHz bands, efficient improvement in gain and noise figure is observed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".