Artificial Neural Network-Based Complex Gain Technique for Digital Predistortion of Power Amplifiers
Bibliographic record
Abstract
This paper proposes an artificial neural network (ANN)-based complex gain technique for digital predistortion (DPD) of power amplifiers (PAs). Existing look-up table (LUT)-based complex gain approach is one of the accurate DPD methods for PA linearization. However, this LUT-based approach consumes lots of memory and the linearization procedure is relatively slow due to the frequently search of the LUT. To address these issues, the proposed ANN-based complex gain technique uses ANN to learn the complicated relationships in the LUT and replaces the LUT with the trained ANN model, effectively minimizing the number of coefficients in the memory and avoiding indexing the LUT. The proposed technique decreases the memory requirement and speeds up the linearization procedure, while maintaining high linearization performance for PAs. The proposed technique is illustrated by two examples, i.e., a Freescale PA and a Doherty PA. The results show that the proposed technique has good linearization capability comparable to the existing LUT-based complex gain approach and can save data storage room as much as 99.65% in the Freescale PA example and 99.87% in the Doherty PA example.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".