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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

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Published on: June 1, 2022

Comparison of Feature Selection Techniques for Power Amplifier Behavioral Modeling and Digital Predistortion

Abdoul Barry¹, Wantao Li¹, Juan A Becerra²

¹Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC)-Barcelona Tech, 08034 Barcelona, Spain.

Sensors (Basel, Switzerland)

|September 10, 2021

Summary

Digital predistortion (DPD) for power amplifiers (PAs) struggles with complex models. This study benchmarks dimensionality reduction techniques, finding greedy pursuits like Doubly Orthogonal Matching Pursuit (DOMP) offer the best balance of speed and accuracy.

Keywords:

behavioral modeling digital predistortion linearization dimensionality reduction feature selection techniques power amplifier

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Area of Science:

Electrical Engineering
Signal Processing
Telecommunications

Background:

Power amplifiers (PAs) are crucial for linearity and efficiency.
Digital predistortion (DPD) mitigates nonlinearities near saturation.
Volterra series models in DPD lead to high coefficient counts and overfitting.

Purpose of the Study:

To benchmark various dimensionality reduction techniques for DPD models.
To compare modeling and linearization performance against runtime.
To identify optimal methods for efficient and robust DPD.

Main Methods:

Categorization of techniques into greedy pursuits (OMP, DOMP, SP, RF), regularization (Ridge, LASSO), heuristic local search (HC, DMS), and global optimization (SA, GA, adaLIPO).
Comparative analysis based on modeling accuracy, linearization performance, and computational runtime.
Evaluation of techniques for reducing the dimensionality of Volterra series models in DPD.

Main Results:

Greedy pursuits demonstrate strong performance in dimensionality reduction.
Doubly Orthogonal Matching Pursuit (DOMP) shows a superior trade-off between execution time and linearization robustness.
Other techniques like regularization and global optimization present different performance characteristics.

Conclusions:

Dimensionality reduction is essential for practical DPD implementation.
DOMP emerges as a highly effective technique for balancing efficiency and performance in DPD.
The benchmark provides valuable insights for selecting appropriate DPD order reduction methods.