Behavior Modeling and Digital Predistortion of Mismatched Wireless Transmitters Using Convolutional Neural Networks
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
In modern wireless compact transmitters, Power Amplifier (PA) behavior is considerably affected by the impedance mismatch between the PA’s output and the antenna’s input. This PA’s output mismatch results in a reflection at the PA–antenna interface. In this brief, reflection-aware PA modeling and digital predistortion (DPD) techniques are proposed to mitigate the negative impact of this mismatch on the forward and reverse models of the PA. An Augmented Convolutional neural network model <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(\boldsymbol{\Gamma }$ </tex-math></inline-formula> ACNN) is proposed to linearize a Doherty PA under different values of the output mismatch using a single set of coefficients. The developed DPD shows robust performance metrics like normalized mean square error (NMSE), and adjacent channel power ratio (ACPR) under diverse complex output mismatch levels.
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) | 0.000 | 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