Linearization of Concurrent Tri-Band Transmitters Using 3-D Phase-Aligned Pruned Volterra Model
Bibliographic record
Abstract
This paper reports a novel digital predistortion (DPD) scheme for concurrent tri-band power amplifiers (PAs). The proposed tri-band DPD is based on a pruned Volterra model that takes into account the impact of the phase distortion observed in multi-band PAs as well as the compound amplitude distortion. By taking into account the phase variation effects across a wide frequency band, the proposed 3-D phase-aligned Volterra DPD can effectively compensate for the crosstalk effects between the fundamental frequencies, their harmonics, and intermodulation products due to the nonlinearity the tri-band PA exhibited. The performance of the proposed DPD is validated using a broadband Class-AB PA driven concurrently by three independent carrier-aggregated long-term evolution signals at separation frequencies around 100 MHz. The measurement results validate the accurate performance of the proposed 3-D phase-aligned pruned Volterra DPD in suppressing the in-band and cross-band intermodulation effects, and shows improvement over a basic 3-D tri-band DPD model that neglects phase variation effects across frequency.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".