Power Amplifier Behavioral Modeling by Neural Networks and Their Implementation on FPGA
Bibliographic record
Abstract
In this paper, field programmable gate array (FPGA) implementation of two power amplifier (PA) dynamic behavioral modeling approaches with real-valued time-delay neural network (RVTDNN) and real-valued recurrent neural network (RVRNN) architectures are presented. The proposed PA models are based on the multilayer perceptron (MLP) neural networks with delayed inputs to take into account nonlinearity and memory effects of the PA. The synoptic weights of these neural networks are dynamically updated in order to prevent any eventual change in the PA's characteristics. Both architectures have been optimized to include only six hidden neurons and implemented on FPGA using Xilinx system generator. The FPGA is preferred to the digital signal processor (DSP) because it allows parallel computation tasks and software like flexibility. The modeling performances of these architectures are compared using 16-QAM modulated test signal. The mean square error (MSE) between the desired and the actual outputs of these models are also compared.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".