Channel-Selective Multi-Cell Digital Predistorter for Multi-Carrier Transmitters
Bibliographic record
Abstract
This paper demonstrates a new channel-selective multi-cell processing predistortion technique that compensates for the nonlinearities of multi-carrier transmitters. The proposed technique uses independent processing cells to compensate for the intra-band and inter-band distortions of nonlinear transmitters. This frequency-selective feature of the proposed technique significantly reduces the minimum sampling rate requirements of analog-to-digital and digital-to-analog converters, which are a critical issue for conventional digital predistortion (DPD) techniques dealing with wideband signals. The proposed technique was evaluated with four-carrier (1001) and six-carrier (100001) WCDMA signals, using a nonlinear 10-Watt power amplifier. The performance of the proposed technique was compared with look-up table, multi-branch and recently proposed frequency-selective DPDs, in terms of adjacent-channel power ratios (ACPRs) and sampling rate requirements. The proposed technique improved the ACPR and the carrier-to-intermodulation power ratio (CIMPR) of the 1001 WCDMA signal by more than 13 dB and 10 dB, respectively.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".