LMI-Based $\mathcal{H}_{\infty}$ Fuzzy T-S Compensator for Intermodulation Distortion in Power Amplifiers
Bibliographic record
Abstract
Power amplifiers (PAs) exhibit an intrinsic nonlinear behavior that can hinder their application to highpower radio frequency transmission. Such characteristic of PAs produces severe output signal distortion when the input signal achieves a certain amplitude, thereby limiting their efficiency to maintain a linear behavior. This problem becomes critical with modern technologies, such as 5 G or 6 G, which employ wide-bandwidth signals, thus implying drastic spectral regrowth phenomena and intermodulation distortion (IMD). In practical terms, the PA spectral efficiency is degraded and the input information becomes corrupted. This paper proposes a control system design based on reference tracking to address this problem. The input-output mapping of a PA is modeled through a state-space representation derived from a classical Volterra series. Then we employ fuzzy Takagi-Sugeno (T-S) representation to exactly describe the nonlinear behavior of the PA in terms of a convex combination of linear local models. This approach enables the design of a fuzzy dynamic output feedback controller using linear matrix inequalities (LMIs) and considering the minimization of the$\mathcal{H}_{\infty}$norm between the input reference and the PA output. Simulation results demonstrate that the proposed technique is able to mitigate the open-loop PA distortion effects, as evidenced by the attenuation of IMD components, especially within the input signal bandwidth.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".