Distortion and impairments mitigation and compensation of single‐ and multi‐band wireless transmitters (invited)
Bibliographic record
Abstract
Transmitter digital domain linearisation solutions are constantly evolving to extract optimum benefits in terms of cost, performance and flexibility. This study highlights the current and futuristic trends in the arena of transmitter system modelling and digital domain compensation for radio frequency distortions of non‐linear power amplifiers and wireless transmitters in case of single‐band wireless transmitter, which is further extended to a dual‐band transmitter. Indeed, the dual‐band transmitter results in severe non‐linear distortion as compared to the single‐band transmitter when operated in concurrent mode, due to the additional inter‐modulation products generated by the dual‐band transmitted signals. Hence, in addition to the intermodulation products and memory effects exhibited in a single‐band transmitter, the cross‐modulation effects of the dual‐band signals should be compensated for as well in a dual‐band transmitter. The effects of the modulator imperfections such as gain and phase imbalances, phase errors and DC offsets and their impact on the feedback loop of a digital predistortion system are also analysed. A thorough comparison between the state‐of‐the‐art behavioural models and their application to digital predistortion technique of power amplifier is presented in terms of performance and complexity. Model performance assessment is discussed through simulation and experimental results for both single‐ and dual‐band systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".