Statistics-Based Approach for Blind Post-Compensation of Modulator’s Imperfections and Power Amplifier Nonlinearity
Bibliographic record
Abstract
Power amplifier (PA) nonlinearity and in-phase and quadrature-phase (I/Q) imbalance are major concerns for wireless transmitters. In this paper, we present a new closed-form expression for the probability density function (PDF) of I and Q components in the presence of transmitter's impairments and propose a blind post-compensation approach for the mitigation of these impairments. These impairments include static PA nonlinearity and frequency-independent I/Q imbalance. The accuracy of the analytical PDF is evaluated using Kullback-Leibler divergence and Hellinger square distance. Simulation results show a reasonable correspondence between the derived PDF and non-parametric kernel density estimation-based PDF. After a closed-form PDF is obtained, higher order statistics-based method is used to estimate PA nonlinearity in the presence of I/Q impairments. Finally, a maximum-likelihood estimation of I/Q imbalance parameters is obtained using the analytical PDF. Simulation results show a normalized mean-squared error (NMSE) of around -40 dB and an adjacent channel power ratio of around -53 dBc, along with an error vector magnitude (EVM) of around 1%, for a 3-MHz local thermal equilibrium signal. Using laboratory measurements, an NMSE of around -35 dB and an EVM of 1.5% are achieved.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".