DESIGN AND OPTIMIZATION OF A FULLY DIFFERENTIAL CMOS VARIABLE-GAIN LNA WITH DIFFERENTIAL EVOLUTION ALGORITHM FOR WLAN APPLICATIONS
Bibliographic record
Abstract
In this paper, we optimized the performance of a 2.4 GHz variable gain low-noise amplifier for WLAN applications which provides high dynamic range with relatively low power consumption. First, the differential evolution algorithm was used to optimize the width of input transistors, then the tunable on-chip switching stage method was applied to control the amplifier gain when the input signal increases. The optimization was performed in terms of gain, noise figure (NF), IIP3 and power dissipation. The LNA has achieved a variable gain from 16.55 to 20.45 dB with excellent NF between 1.63 and 1.74 dB. Furthermore, the proposed circuit achieves a third order input intercept point of 6.6 dBm. It consumes only 10 mW from a 1.5 V supply.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".