On the Modeling and Linearization of a Concurrent Dual-Band Transmitter Exhibiting Nonlinear Distortion and Hardware Impairments
Bibliographic record
Abstract
This paper proposes a novel, complexity-reduced, dual-input, two-box model for the modeling and digital predistortion of a dual-band power amplifier (PA) in the presence of in-phase/quadrature (I/Q) modulator imperfections. The model is composed of two cascaded nonlinear blocks. The first block is implemented as a mildly nonlinear dual-input truncated Volterra filter, which includes second-order cross-terms for the mutual characterization of the dynamic mildly nonlinear memory effects exhibited by the dual-band PA and for the compensation of I/Q imperfections. The second block is implemented as a two-dimensional look-up table for the characterization of the static nonlinearity of the dual-band PA. The proposed model was evaluated through the excitation of the dual-band Doherty PA by two concurrent multi-carrier signals, applied at 880 MHz and 1978 GHz, in the presence of I/Q modulator imperfections. The experimental results showed the accurate performance of the proposed model in suppressing the adjacent channel error power, when compared to other state-of-the-art models, the computational complexity of the proposed model was significantly reduced, which economizes the resources utilized by the model for implementation on a digital signal processing/field-programmable gate array platform.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".