Augmented Real-Valued Time-Delay Neural Network for Compensation of Distortions and Impairments in Wireless Transmitters
Bibliographic record
Abstract
A digital predistorter, modeled by an augmented real-valued time-delay neural network (ARVTDNN), has been proposed and found suitable to mitigate the nonlinear distortions of the power amplifier (PA) along with modulator imperfections for a wideband direct-conversion transmitter. The input signal of the proposed ARVTDNN consists of Cartesian in-phase and quadrature phase ( I/Q ) components, as well as envelope-dependent terms. Theoretical analysis shows that the proposed model is able to produce a richer basis function containing both the desired odd- and even-order terms, resulting in improved modeling capability and distortion mitigation. Its actual performance has been validated through extensive simulations and experiments. The results show that the compensation and hardware impairment mitigation capabilities of the ARVTDNN are superior to the existing state-of-the-art real-valued focused time-delay neural network (RVFTDNN) by 3-4 dB for the adjacent channel power ratio and by 2-3 dB in terms of the normalized mean square error. Other important features of the proposed model are its reduced complexity, in terms of the number of parameters and floating-point operations, and its improved numerical stability compared to the RVFTDNN 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 | 0.000 | 0.000 |
| 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".