A fast adaptive predistorter for nonlinearly amplified M-QAM signals
Bibliographic record
Abstract
M-QAM has been considered to achieve high bandwidth efficiency for broadband wireless communications. However, due to its envelope fluctuation, it exhibits large spectral re-growth and performance degradation when the transmit power amplifier operates in a nonlinear region close to saturation. In this paper, an adaptive predistortion technique suitable for DSP implementation at the baseband signals is introduced to counter-balance the AM/AM and AM/PM nonlinear effects of the transmit power amplifier. Based on nonlinear adaptive Volterra filtering, the proposed pre-distortion technique shows that M-QAM can be used with a transmit power amplifier operating near saturation to a highest power efficiency, while its transmitted spectrum and performance are kept close to those in a linear channel. The convergence behavior of the adaptive predistortion technique is analyzed. The spectral re-growth and performance of a 16 QAM system using a predistorter/SSPA are evaluated using simulation. The adaptive predistortion technique has a low complexity and fast convergence.
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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) | 0.001 | 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".