Design Methodology Proposal of Digital Predistorter Using Matlab and Modelsim Cosimulation
Bibliographic record
Abstract
This paper details the design of a Digital Predistorter (DPD) based on the Simplified Volterra Series (SVS) model. Our main contributions concern first the design of the predistorter unit using the Look Up Table (LUT) method without additional algorithms to decrease the high number of coefficients required for the PA model. Then, a Matlab and Modelsim cosimulation approach is discussed and performed to evaluate the proposed DPD architecture, in particular synthesis results are presented in terms of required Field Programmable Gate Array (FPGA) resources to implement the proposed predistorter. In addition, the performances of the proposed design are verified using a class AB GaN Power Amplifier (PA) driven by one carrier Long Term Evolution-Advanced (LTE-A) signal with 20 MHz channel bandwidth. It is proven that the LUT predistorter occupies only 55 % of the multipliers (DSP48E1) available in the Zynq-7000 FPGA. Also, the Adjacent Channel Power Ratio (ACPR) attains more than -45 dB.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".