Neural network based power amplifier dynamic modeling for wireless communications
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we present the Neural Network (NN) based dynamic Power Amplifier (PA) modeling with memory effect. We developed this model with System Generator for DSP by Xilinx that could be implemented on DSP chip. The advantage of our System Generator based model is to develop highly parallel systems with the most advanced FPGAs, providing system modeling and automatic code generation from Simulink and MATLAB. Our real time modeling method can be adapted for any kind of latest signal type such as cdma-2000 and W-CDMA without any modification of the model and can be adapted to any environmental change such as temperature variation in PA without modify the model. That means our real time model is self adaptable. By using this method we can do a modeling dynamically the non linearity of the PA including memory effects for realistic modulation signals inputs. In this paper, by using our modeling architecture we demonstrate to have an almost same dynamic AM-AM and AM-PM curves of PA.
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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 it