Systematic and Adaptive Characterization Approach for Behavior Modeling and Correction of Dynamic Nonlinear Transmitters
Bibliographic record
Abstract
This paper proposes a comprehensive and systematic characterization methodology that is suitable for the forward and reverse behavior modeling of wireless transmitters (Txs) driven by wideband-modulated signals. This characterization approach can be implemented in adaptive radio systems since it does not require particular signal or training sequences. The importance of the nature of the driving signal and its average power on the behavior of radio-frequency Txs are experimentally investigated. Critical issues related to the proposed characterization approach are analytically studied. This includes a new delay-estimation method that achieves good accuracy with low computational complexity. In addition, the receiver linear calibration and its noise budget are investigated. To demonstrate the accuracy and robustness of the proposed method, a full characterization (including the memoryless nonlinearity and the memory effects) of a 100-W Tx driven by a multicarrier wideband code-division multiple-access signal is carried out, and its forward and reverse models are identified. Cascading the identified reverse model derived using the proposed methodology and the Tx prototype leads to excellent compensation of the static nonlinearities and the memory effects exhibited by the latter. Critical issues in implementing this approach are also discussed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".