Flexible test bed for the behavioural modelling of power amplifiers
Bibliographic record
Abstract
Purpose – The paper aims to focus on the memory-polynomial model (MPM) as special case of Volterra series, implemented in hardware. The behavior of the MPM is fully proved through a comparison with AM-AM and AM-PM measured data. The results show that this simulation technique is able to prove the effectiveness of the MPM implementation as behavioural model for high power radiofrequency amplifiers. The system is able to compensate perturbations caused by modern communication systems. Design/methodology/approach – The implementation uses Matlab-Simulink, and its digital signal processing (DSP) builder. The first stage allows developing the model in Matlab using the DSP builder blockset through the signal compiler block. Then, the design is downloaded to the DSP board. Findings – The paper demonstrates a proper behavior of the MPM as a truncation of the Volterra series, with respect to different inputs. This is a key point, because the series truncations allow first to implement this model in real time and second to obtain a correct precision, for instance when modeling amplification of digital signals in high frequency. Originality/value – The global system approach permits to easily develop, simulate, and validate a wireless system. The efficiency of a complete connected solution based on Agilent Technologies tools, combining simulations and measurements under true operating conditions, seems to be clearly demonstrated.
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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 | 0.000 |
| 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) | 0.006 | 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".