Power amplifier linearisation using digital predistortion and multi‐port techniques
Bibliographic record
Abstract
Power amplifiers are essential components in communication systems and are inherently non‐linear. The non‐linearity creates spectral growth (broadening) beyond the signal bandwidth, which interferes with adjacent channels. It also causes distortions within the signal bandwidth, which decreases the bit error rate at the receiver. This study reports an adaptive digital predistorter with fast convergence rate and low complexity and cost to alleviate these problems. In this design, a lookup table‐based adaptive digital predistortion (DPD) technique using a five‐port receiver instead of traditional heterodyne and homodyne architectures is proposed to realise the linearisation loop for this amplifier. The five‐port receiver is implemented by use of passive microwave circuits and detector diodes. This approach highly reduces the cost and complexity of the linearisation system. Simulation and measurement results obtained are presented for a laterally diffused metal–oxide–semiconductor‐based high‐power amplifier biased in class AB operation with wideband code‐division multiple access input signal to demonstrate the effectiveness of this novel DPD design. Moreover, these results are compared with DPD technique using homodyne receiver in feedback path.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".