Low Complexity Distributed Model for the Compensation of Direct Conversion Transmitter’s Imperfections
Bibliographic record
Abstract
In modern communication systems, nonlinearity in power amplifiers (PAs) and in-phase and quadrature-phase (I/Q) imperfections in the transmitter are of enormous concern. With the increase in the importance for highly energy efficient and low complexity models, there is a need to develop low complexity digital predistortion (DPD) methods. In this paper, we present a novel memory polynomial based distributed two block model to alleviate these impairments. Various performance metrics are used to evaluate the design performance and complexity of proposed model as compared to the state of the art predistorter model. Simulation and measurement results indicate the ability of the proposed model to meet the desired design purpose with reduced complexity in terms of number of coefficients, dispersion coefficient, condition number and number of floating points operations required for computing various steps in the inverse modeling algorithm. This is achieved while maintaining reasonable performances in terms of NMSE and ACEPR. The major attribute of the model is the reduction in complexity of the system. The number of complex valued coefficients and the number of floating point operations (FLOPs) are both reduced by 17%-56%, matrix conditioning is improved by 12-33 dB and the dispersion coefficient is reduced by 16-42 dB as compared to the previously proposed joint modulator and power compensation technique.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".