A Least-Squares Volterra Predistorter for Compensation of Non-Linear Effects with Memory in OFDM Transmitters
Bibliographic record
Abstract
This paper proposes a general solution to compensate for the nonlinearity of a high power amplifier (HPA) with memory effects in orthogonal frequency division multiplexing (OFDM) communication systems at the transmitter side. Mean square error (MSE) minimization is used to derive the predistorter, which is modelled as a simplified finite order Volterra system, A general feedback loop structure is used in order to reduce the computational complexity. The input and output of the nonlinear system with memory effects are accessed, and a least-squares solution is used to obtain the Volterra kernel, which represents the predistorter. Once the Volterra kernel is obtained, when signals pass through the cascaded system of the predistorter and the HPA, overall linear system characteristics are achieved. The advantage of the proposed method is that it does not assume any specific model for the HPA. The performance of the proposed scheme is verified through computer simulations. Specifically, the improvements in the reduction of out-of-band spectral regrowth and enhanced performance in terms of the bit error rate (BER) are documented for the travelling wave tube (TWT) HPA model.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".