Improved Compensation of HPA Nonlinearities Using Digital Predistorters with Dynamic and Multi-dimensional LUTs
Bibliographic record
Abstract
Orthogonal frequency division multiplexing (OFDM) has been widely adopted in communications systems due to its high spectral efficiency and resistance to multipath fading and impulse noise. However, because of its large peak-to-average power ratio (PAPR), OFDM is highly sensitive to nonlinear distortion. In wireless systems, where to achieve acceptable power efficiency, the high power amplifiers (HPAs) operate near the saturation point, OFDM signals cause spectral regrowth leading to prohibitively high levels of adjacent channel interference (ACI). This paper investigates two signal predistortion techniques that attempt to mitigate these effects. The first approach assumes the HPA is memoryless, meaning that the output only depends on the current input. A predistorter using a one-dimensional (1-D) look up table (LUT) which has non-uniform bin spacing in amplitude is proposed. This non-uniform spacing allows the LUT to produce a better estimate of the inverse gain characteristic of the HPA. The second approach attempts to utilizes a two- dimensional (2-D) LUT which is capable of linearizing amplifiers with memory effects where the amplifier gain has a dependency on the short-term average power of the input signal. Multiple LUTs are constructed over the range of the input signal with each LUT being employed over a specific range of short-term average power.
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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) | 0.001 | 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 | 0.000 |
| 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".