Extended Hammerstein model for RF power amplifier behavior modeling
Bibliographic record
Abstract
this paper presents a novel Extended Hammerstein model for predicting the dynamic nonlinearity of wideband Radio Frequency Power Amplifier (RFPA). This new model tackles the inaccuracy of Conventional Hammerstein scheme in accounting for the short term memory effects of the RFPA by adding an extra branch. For that, an error signal between the output and the input signal of the memoryless submodel is first computed and processed by an additional filter. The filtered error signal is then post-injected at the output of the model. This extra modeling mechanism leads to remarkable accuracy when modeling two different RFPAs operating at different biasing points and driven with 4 Carrier (4C) WCDMA signals. Despite its simple structure and identification algorithm, the Extended Hammerstein model demonstrated excellent capacity in mimicking the dynamic characteristics (AM/AM & AM/AM) and the output signal spectrum of the RFPA under test.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".