Journal of Telecommunications and Information Technology, 2011, nr 4

kwartalni

Comparison of hill-climbing and genetic algorithms for digital predistortion models sizing

International audienc

Comparison of feature selection techniques for power amplifier behavioral modeling and digital predistortion linearization

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.This work was supported in part by the Spanish Government (Ministerio de Ciencia, Inno- vación y Universidades) and Fondo Europeo de Desarrollo Regional (FEDER) under Grants TEC2017- 83343-C4-2-R and PID2020-113832RB-C21 and in part by the Government of Catalonia and the European Social Fund under Grants 2017-SGR-813 and 2021-FI-B-137.Peer ReviewedPostprint (published version