Behavioral modeling of high power RF amplifiers using pruned Volterra scheme with IIR basis functions

A low-order nonlinear amplifier model with distributed delay terms

“…A mathematical representation of the input and output is given in Eqs. (12) and (13). There is a 10-dB power difference between the inner and outer tones.…”

“…Among these * Correspondence: hayrettinyuzer@gmail.com published studies [4,[6][7][8][12][13][14] are baseband and [2,3,5,9,11] are passband modeling techniques. Mathematical definitions and performance comparisons of the models were also published for baseband [15][16][17][18] and passband modeling [18].…”

“…There are a number of behavioral modeling methods published in the literature, such as the Saleh model [2], general power series expansion [3][4][5], memoryless polynomial modeling [6], look-up-table modeling [7,8], Volterra series [9], Wiener-Hammerstein (3-box) [10,11], Volterra [12,13], and neural networks [14]. Among these * Correspondence: hayrettinyuzer@gmail.com published studies [4,[6][7][8][12][13][14] are baseband and [2,3,5,9,11] are passband modeling techniques. Mathematical definitions and performance comparisons of the models were also published for baseband [15][16][17][18] and passband modeling [18].…”

FPGA-based test bed for measurement of AM/AM and AM/PM distortion and modeling memory effects in RF PAs

“…These conditions necessitate the modeling of the memory effects added by the PA. For instance, several kinds of behavioral modeling approaches have been explored, such as memory and memoryless models [1,2], polynomial models [3], and neural networks [4,5]. Other models are classic Volterra series [6][7][8] or variations of Volterra series models [9] that provide a proper method of modeling the nonlinear order and memory effects. Ref.…”

Modeling memory effects in RF power amplifiers applied to a digital pre-distortion algorithm and emulated on a DSP-FPGA board

“…That way, to mitigate undesired intermodulation and distortion effects, recent advances related to linearization techniques have been developed, mainly to compensate memory effects induced by real PAs [2,3]. These issues have been partially solved introducing modeling techniques for PAs, such as: memory or memoryless models [4,5], polynomial models [6], neural networks [7,8], others techniques taking into account memory effects based on Volterra series [9][10][11][12][13], etc., all of them providing a proper way to capture the nonlinearity order and memory depth.…”