Neural networks and volterra series for time-domain power amplifier behavioral models

✍ Scribed by F. Giannini; P. Colantonio; G. Orengo; A. Serino; G. Stegmayer; M. Pirola; G. Ghione

• Publisher: John Wiley and Sons
• Year: 2007
• Tongue: English
• Weight: 450 KB
• Volume: 17
• Category: Article
• ISSN: 1096-4290
• DOI: 10.1002/mmce.20210

✦ Synopsis

This paper presents a black-box model that can be applied to characterize the nonlinear dynamic behavior of power amplifiers (PAs), including strong nonlinearities and memory effects. Feedforward time-delay Neural Networks (TDNN) are used to extract the model from a large-signal input-output time-domain characterization in a given bandwidth; furthermore, explicit formulas to derive Volterra kernels from the TDNN parameters are also presented. The TDNN and related Volterra models can predict the amplifier response to different frequency excitations in the same bandwidth and power sweep. As a case study, a PA, characterized with a two-tone power swept excitation, is modeled and simulations are found in good agreement with training measurements; moreover, a model validation with two tones of different frequencies and spacing is also performed. V