This work considers behavioral modelling of radio frequency power amplifiers. Due to the use of modern digital modulation methods power amplifiers are nowadays subjected to signals having a considerable bandwidth and a fast changing envelope. This means that traditional quasi-memoryless amplitude-to-amplitude (AM/AM) and amplitude-to-phase (AM/PM) characteristics are no longer enough to describe and model the behavior of power amplifiers, neither can they be successfully used for linearization.

In this thesis, sampled input and output data are used for identification and validation of some block structure models with memory. The time-discrete Volterra model, the Wiener model, the Hammerstein model, and the radial-basis function neural network are all identified and compared with respect to in-band and out-of-band errors. Two different signal types, i.e. multi tones and noise, with different powers, peak-to-average ratios, and bandwidths have been used as input to the amplifier. Two different power amplifiers were investigated, one designed for the third generation mobile telecommunication systems and one for the second generation.

A stepped three-tone measurement technique based on digitally modulated baseband signals is presented. The third-order Volterra kernel were determined from identified inter-modulation products. The properties of the Volterra kernel along certain parts in the three dimensional frequency space were analysed and compared to the Wiener and Hammerstein models.