Complexity reduced odd-order memory polynomial pre-distorter for 400-watt multi-carrier Doherty amplifier linearization
Bibliographic record
Abstract
this paper expounds on the computation complexity reduction of memory polynomial (MP) digital predistorter (DPD) construction and implementation as needed to linearize power amplifiers (PA) when driven with multi-carrier signals. For that, the even-order terms in the MP branches were first removed. Then, the PA memory effects theory was used to further reduce the number of coefficients of the MP-DPD by decreasing the nonlinearity orders in the different branches individually. These two steps allowed for a reduction of the number of coefficients to almost one-third and the conditioning number by three orders of magnitude while maintaining the same linearization capability. This substantially alleviates the requirements on the digital signal processors and the time needed to construct and implement the MP-DPD in real environment. Experimental validation carried out using a 400Watt Doherty Power Amplifier (DPA), driven with 4-Carrier WCDMA signal, showed excellent linearization capability by achieving an ACPR of better than 50dBcwith a power efficiency of better than 42.4%.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".