Radio Frequency Power Amplifiers Behavioral Modeling, Parameter-Reduction, and Digital Predistortion

This work considers behavioral modeling, parameter-reduction, and digital predistortion of radio frequency power amplifiers. Due to the use of modern digital modulation methods, contemporary power amplifiers are frequently subjected to signals characterized by considerable bandwidths and fast changing envelopes. As a result, traditional quasi-memoryless amplitude-to-amplitude (AM/AM) and amplitude-to-phase (AM/PM) characteristics are no longer sufficient 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 several 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 (multitones and noise), with different powers, peak-to-average ratios, and bandwidths have been used as inputs to the amplifier. Furthermore, two different power amplifiers were investigated, one designed for third generation mobile telecommunication systems and one for second generation systems. A stepped three-tone measurement technique based on digitally modulated baseband signals is also presented. The third-order Volterra kernel parameters were determined from identified intermodulation products. The symmetry properties of the Volterra kernel along various portions of the three dimensional frequency space were analyzed and compared with the symmetry of the Wiener and Hammerstein systems. The Kautz-Volterra model, a new type of behavioral model for radio frequency power amplifier modeling, is proposed and compared with existing behavioral models with respect both to accuracy and complexity. The proposed model has the same general properties as the Volterra model and has been shown experimentally to be appropriate for use as a either direct or inverse model. Furthermore, the high accuracy of the Kautz-Volterra model allows use of a frequency weighting algorithm to suppress the out-of-band error to low levels. This thesis gives the experimental results of the Kautz-Volterra model used with frequency weighted data as well as reports on the use of this model as a digital predistortion algorithm for radio frequency power amplifiers. The main results in this thesis include two novel behavioral radio frequency power amplifier models, a method to measure and analyze Volterra kernels along certain paths in the frequency domain, and the conclusion that when designing a PA for use with digital predistortion it is most important to reduce the memory effects in the linear term since these will have the most significant impact on the …