Behaviour modelling of wideband RF transmitters using Hammerstein–Wiener models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The authors concern the application and use of a class of block-oriented systems, known as Hammerstein–Wiener, to model the dynamic non-linear behaviour of radio frequency (RF) transmitters. This class of system models is found to better mimic strong dynamic non-linear systems, as compared to simpler individual Hammerstein and Wiener models. The identification, which may look complicated at first glance, is skillfully tackled in an iterative two-stage identification approach by applying a class of global pattern search techniques along with the Narendra–Gallman method. The number of parameters processed at each iteration is therefore reduced, which considerably facilitates the progress and convergence of the identification algorithm. The model is identified and validated for two classes of power amplifiers. The validation results are obtained for various orders and are compared to the results of memory polynomial models. The Hammerstein–Wiener model achieves comparable results with a much lower level of complexity, in terms of the number of parameters associated with the 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.
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) | 0.001 | 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 | 0.000 |
| 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 it