Efficient Compensation of the Nonlinearity of Solid-State Power Amplifiers Using Adaptive Sequential Monte Carlo Methods
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Bibliographic record
Abstract
In this paper, the sequential Monte Carlo (SMC) framework is studied as a tool to compensate the nonlinear distortions caused by solid-state power amplifiers (SSPA) in M-QAM schemes. The performance of the SMC approach is shown for low- and high-order constellation schemes for different values of the input backoff (IBO). The results show that, in low-IBO regimes, the SMC method provides a significant improvement compared to conventional methods, such as the predistorter, especially for high-order constellations while the use of the predistorter is preferred in only a very limited number of cases. Moreover, the SMC framework is shown to have more robust behavior to the constellation scaling. The application of the SMC framework to multicarrier systems is also addressed and the behavior of the system in terms of the out-of-band emissions as a function of the output backoff (OBO) is investigated. Finally, an adaptive sequential Monte Carlo receiver is proposed that adapts itself efficiently to the variations in the amplifier parameters. This adaptive scheme does not require a training sequence and does not suffer from convergence problems.
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Full frame distilled prediction
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) | 0.000 | 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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