Compensation of nonlinear distortions with memory effects in digital transmitters
Bibliographic record
Abstract
High-level linear modulation schemes used in modern digital communication systems exhibit large peak-to-average power ratios (PAPRs). The performance of transceivers is very sensitive to nonlinear distortions, which arise mainly from the high power amplifier (HPA). Also, because of the wideband signals, the nonlinear distortions are frequency-dependent. The paper proposes an algebraic solution to compensate at the transmitter for the HPA nonlinearity. The HPA is represented by a memoryless nonlinear block followed by a linear filter. We first estimate the parameters of the unknown nonlinearity, which is modelled through a polynomial expansion. The frequency response of the unknown filter is then calculated, in order to capture the memory effects in the system. Using the identified nonlinear system parameters, a cascade of the inverse filter and the inverse memoryless nonlinearity is constructed preceding the HPA, in order to predistort the input signals and achieve overall linear transmitter characteristics. The scheme is examined through computer simulations for quadrature amplitude modulation (QAM). Improvements in the bit error rate (BER) and out-of-band spectrum regrowth are demonstrated for the travelling wave tube (TWT) HPA model. The results show that the proposed method is effective in compensating for amplitude-to-amplitude (AM/AM) distortions with memory using a relatively-small number of data points at the identification stage.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".