Augmented Dual-Band Digital Predistorter for Reducing Cross-Band Intermodulation Distortion Using Predictive Injection Technique
Bibliographic record
Abstract
In this paper, an augmented dual-band digital predistortion (DPD) technique for reducing the cross-band intermodulation distortion (IMD) using predictive injection technique is proposed to address some of the shortcomings of dual-band DPDs. The technique alleviates the need to observe the cross-band third-order IMD (IMD3) terms in the feedback loop by predicting the distortion terms and generating synthetic signals, which are then injected at the transmitter side. To highlight the issue, the wideband and dual-band DPD architectures and their respective limitations are briefly outlined. The theory behind the proposed concept is developed, and practical measurements performed using a Class AB power amplifier driven by a long-term evolution signal are provided to support the theory. The results obtained show that the proposed method achieves promising performance in mitigating the cross-band IMD3 issue faced by dual-band DPDs. This technique can be extended to mitigate higher order cross-band distortion as well.
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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".