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

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 17; p. 5772
• Main Authors: Barry, Abdoul, Li, Wantao, Becerra, Juan A., Gilabert, Pere L.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 27.08.2021; MDPI
• Subjects: Adaptive algorithms; Algorithms; Behavior; behavioral modeling; digital predistortion linearization; dimensionality reduction; Dynamic models; Feature selection; feature selection techniques; Field programmable gate arrays; Genetic algorithms; Linearization; Matched pursuit; Modelling; Parameter identification; power amplifier; Power amplifiers; Power efficiency; Power management; Principal components analysis; Regularization; Simulated annealing; Statistical analysis; Subsystems
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21175772

The power amplifier (PA) is the most critical subsystem in terms of linearity and power efficiency. Digital predistortion (DPD) is commonly used to mitigate nonlinearities while the PA operates at levels close to saturation, where the device presents its highest power efficiency. Since the DPD is generally based on Volterra series models, its number of coefficients is high, producing ill-conditioned and over-fitted estimations. Recently, a plethora of techniques have been independently proposed for reducing their dimensionality. This paper is devoted to presenting a fair benchmark of the most relevant order reduction techniques present in the literature categorized by the following: (i) greedy pursuits, including Orthogonal Matching Pursuit (OMP), Doubly Orthogonal Matching Pursuit (DOMP), Subspace Pursuit (SP) and Random Forest (RF); (ii) regularization techniques, including ridge regression and least absolute shrinkage and selection operator (LASSO); (iii) heuristic local search methods, including hill climbing (HC) and dynamic model sizing (DMS); and (iv) global probabilistic optimization algorithms, including simulated annealing (SA), genetic algorithms (GA) and adaptive Lipschitz optimization (adaLIPO). The comparison is carried out with modeling and linearization performance and in terms of runtime. The results show that greedy pursuits, particularly the DOMP, provide the best trade-off between execution time and linearization robustness against dimensionality reduction.