Compensating I–Q Imperfections in Hybrid RF/Digital Predistortion With an Adapted Lookup Table Implemented in an FPGA
Bibliographic record
Abstract
The performance of hybrid RF/digital predistortion (RF-DPD) is limited, due to in-phase (I) and quadrature-phase (Q) imperfection in its key component, the RF vector multiplier, and the associated circuitry. These imperfections cause errors, in terms of implemented gain and phase of the predistortion function. This brief presents the methodology of implementing hybrid RF-DPD with a lookup table (LUT) adapted to compensate for hardware related I-Q imperfections of the RF vector multiplier within the digital signal processing domain. This modified LUT will accurately compensate for I-Q imperfection, without needing a precise tuning of the control voltages at the pins of the RF vector multiplier. This brief also presents the test setup for characterizing the RF-DPD system to obtain the I-Q imperfections within it and utilizes this information to modify the LUT to compensate for these imperfections. To verify the capability of the modified LUT in compensating for the I-Q imperfections, an experimental validation is carried out by linearizing a class-AB base station power amplifier using the hybrid RF-DPD system developed with an Altera Stratix field-programmable gate array (FPGA) evaluation board. In addition to the 12-dB adjacent-channel leakage ratio obtained using static RF-DPD, an improvement of 2.5 dB is achieved using the proposed I-Q compensation technique.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".