Memory polynomial with shaped memory delay profile and modeling the thermal memory effect
Bibliographic record
Abstract
This paper presents a proposal for a new memory polynomial modeling technique with non-uniform delay taps to capture the thermal memory effects in power amplifiers. In the proposed modeling structure, each order of the memory polynomial is assigned a different memory delay. The delay profile is an exponentially shaped function, instead of equal unit delays for all memory polynomial branches. Three different metrics, the memory effect modeling ratio (MEMR) and the normalized mean square error (NMSE) and spectrum error (SE) are used to benchmark the proposed exponentially shaped delay profile memorial polynomial model performance against previously published models, namely the memoryless, the unit delay and sparse delay memory based polynomial models. The model coefficients of four models are extracted for three different excitation signals, which were selected as a 64-QAM signal around 2.14 GHz with 20 kHz, 30 kHz and 40 kHz bandwidths, ensuring that the thermal memory effects dominate the electrical memory effects. It is shown that the proposed model outperforms all the previously published models for all three excitation signals used in the experiment.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".