A Uniform Neural Network Digital Predistortion Model of RF Power Amplifiers for Scalable Applications
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 this article, a uniform neural network (NN) digital predistortion (DPD) model of radio frequency (RF) power amplifiers (PAs) is proposed for dynamic applications, which is suitable for RF PAs under various operating conditions without updating the coefficients. With the development of communication systems, it is difficult for the DPD to track the nonlinearity of the PA as the operating condition varies frequently. As one of the most promising achievements in recent years, the NN has shown excellent generalization ability, which is applicable to the DPD for scalable applications. In this situation, a uniform neural network model (UNNM), whose structure is a two-stage network, is proposed for scalable output power, scalable bandwidth, or simultaneous scalable power and bandwidth. The experiments are carried out on two sub-6 GHz broadband GaN Doherty PAs (DPAs). The experimental results show that the proposed model can achieve comparable performance without coefficient update in the scalable output power range of about 5 dB and the bandwidth range of 100 MHz, which outperforms the conventional fixed model with better than 3 dB power range and 40 MHz bandwidth range.
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