Linearization of Active Downconversion Mixers at the IF Using Feedforward Cancellation
Bibliographic record
Abstract
A feedforward linearization technique for third-order intermodulation (IM3) distortion cancellation in active downconversion mixers is proposed in this paper. Low-frequency second-order intermodulation (IM2) tones are created and multiplied with the mixer's output to generate low-frequency IM3replicas for cancellation. Implemented mostly at the IF band, this technique brings a third-order input intercept point (IIP3) improvement independently of the mixer topology and is robust against parasitic parameters. A 2-GHz current commutating mixer linearized by the proposed technique is designed and fabricated using a 130-nm CMOS process to verify the concept. Experimental results show that the mixer with a unit-gain amplifier delivers 8.5 dB of conversion gain and has an IIP3of 2.5 dBm before linearization. The linearization technique improves the mixer's IIP3by 12 dB for input signals as large as -15 dBm. The technique has a negligible impact on the mixer's gain and incurs a noise figure penalty of less than 0.2 dB. The mixer with the unit-gain amplifier consumes a current of 8.4 mA, while the proposed technique circuitry consumes an extra current of 4.2 mA, both using a 1.2-V voltage 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".