Look-up table-based digital predistorter implementation for field programmable gate arrays using long-term evolution signals with 60 MHz bandwidth
Bibliographic record
Abstract
This study discusses the implementation of a digital predistorter to linearise radiofrequency (RF) power amplifiers, using input signals 60 MHz in bandwidth. The digital predistorter characterisation procedure is performed on a digital signal processor, using a memory polynomial modelling technique with QR-based recursive least squares (QR-RLS) as the extraction procedure. A multiple look-up table design for the memory polynomial predistorter is introduced, and by using fixed-point operations, reduces the processing latency considerably when compared with a floating-point-based predistorter implementation on a field programmable gate array (FPGA). Linearisation results are shown for a laterally diffused metal oxide semi-conductor (LDMOS)-based power amplifier (PA) biased in class AB operation with a three-carrier long-term evolution-time division duplex (LTE-TDD) input signal. Combining both the optimised predistortion coefficient extraction and predistorter implementation gives up to 20 dBc improvement in the adjacent channel and meets the wireless communication standard requirements.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".