Hybrid Look-Up-Tables Based Behavioral Model for Dynamic Nonlinear Power Amplifiers
Bibliographic record
Abstract
A new look-up table based behavioral model for dynamic nonlinear power amplifiers is proposed. This model labelled as hybrid look-up tables model is based on the combination of a memoryless look-up table sub-model and a nested look-up tables one. It is demonstrated that the proposed model circumvents the computational complexity associated with the parameters identification in analytically defined behavioral models. Moreover, the proposed model reduces the size of the standalone nested look-up tables model by approximately 80% while maintaining its accuracy. Furthermore, a novel slew-rate based trimming and indexing technique to reduce the nested look-up tables model size is developed and corroborated experimentally. Additionally, the two-box structure of the hybrid look-up tables model makes it suitable for bandwidth scalability. Experimental validation using LTE-advanced test signals with up to 120MHz bandwidth demonstrates the ability of the proposed hybrid look-up tables model to be bandwidth scalable with less than 0.5dB degradation in the normalized mean-squared error.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".