2D curtailed harmonic memory polynomial for reduced complexity in concurrent dual‐band modelling and digital predistortion with the second band at harmonic frequency
Bibliographic record
Abstract
Multi‐band transmitter systems are evolving to support the smooth transition from 4G to 5G communication systems. Moreover, recent developments of multi‐band and ultra‐wideband power amplifiers have led to a possible scenario where the second carrier signal is transmitted at the harmonic frequency of the first carrier signal. This results in harmonic interference from the first carrier signal as well as additional cross‐modulation and intermodulation distortion (IMD) components, which cannot be filtered out. The computational and memory requirements for digital predistortion (DPD) in such scenario increase drastically to include all interference terms. This study presents a novel two‐dimensional curtailed harmonic memory polynomial (2D‐CHMP) model to capture harmonic interferences, cross‐modulation and IMDs. The model complexity and memory requirement of 2D‐CHMP are very less as compared to the state‐of‐the‐art two‐dimensional harmonic memory polynomial (2D‐HMP) model. For proof‐of‐concept, it is shown with two different measurement setups that the proposed 2D‐CHMP DPD provides similar linearisation performances as compared to the 2D‐HMP DPD with less number of coefficients and computational complexity. As a study, it is shown that the proposed model can be further adapted to a low‐precision (low‐bit) environment by utilising principal component analysis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".