A fast adaptive predistorter for nonlinearly amplified M-QAM signals
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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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Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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