Linearization of Power Amplifiers Using the Reverse MM-LINC Technique
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Bibliographic record
Abstract
A new mode-multiplexing linear amplification with nonlinear components (MM-LINC) technique is proposed in this brief. This technique, which is referred to as reverse MM-LINC, consists of using linear amplification with nonlinear components (LINC) hardware, along with two signal decomposition modes, depending on the power of the input signal. This decomposition guarantees acceptable linearity of the signal when the power amplifiers (PAs) are operating at their full power potential. This linearity improvement requires only relatively light computational complexity, which makes this technique a suitable alternative to digital predistortion for mobile and medium-power wireless transmitters. The implementation of the reverse MM-LINC concept for mobile WiMAX applications showed an improvement of 15 dB in the adjacent channel power ratio and a reduction in the error vector magnitude from 10% to 2.2%, when compared with a nonlinearized class-AB PA operating at full power potential. At this operation point, the efficiency of the reverse MM-LINC amplification system was not significantly different from the efficiency of the class-AB PA.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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