Abstract
This paper provides a review of greedy pursuits for optimizing Volterra-based behavioral models structure and estimating its parameters. An experimental comparison of the digital predistortion (DPD) linearization performance achieved by these approaches for model-order reduction, such as compressive sampling matching pursuit (CoSaMP), subspace pursuit (SP), orthogonal matching pursuit (OMP), and the novel doubly OMP (DOMP), is presented. A benchmark of the techniques in the DPD of a commercial class AB power amplifier (PA) and a class J PA operating over a 15-MHz Long-Term Evolution (LTE) signal is presented, giving a clear overview of their pruning characteristics in terms of linearization indicators and regressor selection capabilities. In addition, the benchmark is run in a cross-validation scheme by identifying the DPD with a 30-MHz 5G-new radio (NR) signal and validating with the same signal and a 20-MHz multicarrier wideband code division multiple access (WCDMA) signal. The DOMP is shown to be a promising technique since it achieves an enhanced model-order reduction for a similar linearization performance and precision.
Highlights
E VERY generation of wireless communication systems brings new modulation schemes, higher bandwidths, and more challenging constrains for the transceivers
We focus on greedy algorithms applied to the order reduction of Volterra-based models, providing a standard formulation and a discussion of their similarities and differences
The operation point was set to an output power of +32.8 dBm, in which the power amplifier (PA) exhibits a gain expansion followed by a compression
Summary
E VERY generation of wireless communication systems brings new modulation schemes, higher bandwidths, and more challenging constrains for the transceivers. The baseband model derived for the fundamental frequency output is referred to as the full Volterra (FV) model [4] in order to distinguish it from other pruned Volterra representations with a simplified structure Another general model is the complexvalued Volterra series (CVS) model presented in [5] as an extension of the Volterra series for the case of a nonlinear system with complex-valued signals. Ad hoc pruning strategies are applied without knowledge of the internal structure of the PA, based on an a priori selection This is the case of the widely adopted memory polynomial (MP) [6] and generalized MP (GMP) models [2]. We focus on greedy algorithms applied to the order reduction of Volterra-based models, providing a standard formulation and a discussion of their similarities and differences.
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