Generalized Rational Functions for Reduced-Complexity Behavioral Modeling and Digital Predistortion of Broadband Wireless Transmitters
Bibliographic record
Abstract
In this paper, we present and analyze rational-function-based digital predistortion (DPD) of transmitters for broadband applications where system noise and prominent memory effects contribute to the overall nonlinearity of the system. The performance is reported for simulation and measured results for gallium nitride (GaN)-based class-AB and laterally diffused MOS (LDMOS)-based Doherty power amplifiers (PAs) using three different wideband code division multiple access signals with peak-to-average-power ratios of around 10 dB. The performance of the proposed model, in terms of normalized mean-square error, adjacent channel power ratio, matrix condition number, and coefficient dispersion, is compared against those of a memory polynomial (MP) model and a previously proposed rational-function-based model. It is shown by simulation and measurement that the previously proposed absolute-term denominator rational functions have limitations in the inverse modeling needed for DPD. A new variation of the rational function is proposed to alleviate this limitation. Depending on the type of PA and signals, a floating-point operation reduction of 8%-38% is reported as compared with a low-complexity MP model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".