Efficient Compensation of the Nonlinearity of Solid-State Power Amplifiers Using Adaptive Sequential Monte Carlo Methods
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".