Compensation of nonlinear distortions with memory effects in OFDM transmitters
Bibliographic record
Abstract
The OFDM modulation scheme is characterized by the Gaussian-like signal behavior with a relatively high peak-to-average power ratio (PAPR). As a result, it is very sensitive to nonlinear distortions, which arise mainly from the high power amplifier (HPA). This paper proposes an algebraic solution to compensate at the transmitter for nonlinearity of the HPA with memory effects, where the HPA behavioral model is represented by the Hammerstein structure, a cascade of a memoryless nonlinear block followed by a linear filter. In particular, a frequency domain parameter identification methodology is developed that first estimates 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, an inverse Wiener structure, consisting of a cascade of the inverse filter and the inverse memoryless nonlinearity, is constructed preceding the HPA, in order to predistort the input signals so as to achieve overall linear transmitter characteristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".