School of Electrical and Computer Engineering Georgia Institute of Technology, 2004. - 102 с.Power amplifiers are essential components in communication systems and are inherently nonlinear. The onlinearity reates spectral growth (broadening) beyond the signal bandwidth, which interferes with adjacent channels. It also causes distortions within the signal bandwidth, which decreases the bit error rate at the receiver. Newer transmission formats, such as wideband code division multiple access (WCDMA) or orthogonal frequency division multiplexing (OFDM), are especially vulnerable to the nonlinear distortions due to their high peak-to-average power ratios (PAPRs). If we simply back-off the input signal to achieve the linearity required for the power amplifier, the power amplifier efficiency will be very low for high PAPR signals.
Another choice is to linearize a nonlinear power amplifier so that overall we have a linear and reasonably efficient device. Digital predistortion is one of the most cost effective ways among all linearization techniques. However, most of the existing designs treat the power amplifier as a memoryless device. For wideband or high power applications, the power amplifier exhibits memory effects, for which memoryless predistorters can achieve only limited linearization performance.
In this dissertation, we propose novel predistorters and their parameter extraction algorithms. We investigate a Hammerstein predistorter, a memory polynomial predistorter, and a new combined model based predistorter. The Hammerstein predistorter is designed specifically for power amplifiers that can be modeled as a Wiener system. The memory polynomial predistorter can correct both the nonlinear distortions and the linear frequency response that may exist in the power amplifier. It is a robust predistorter, which has demonstrated good performance on several nonlinear system models. Real-time implementation aspects of the memory polynomial predistorter are also investigated in the dissertation.
The new combined model includes the memory polynomial model and the Murray Hill model, thus extending the predistorter’s ability to compensate for strong memory effects in the power amplifier. Performance of the new model is demonstrated through experimental measurements.
The predistorter models considered in this dissertation include both even- and oddorder nonlinear terms. In the literature, most of the power amplifier and predistorter models consider only the odd-order terms. Here, we show that it is beneficial to include even-order nonlinear terms in both the baseband power amplifier and predistorter models. By including these even-order nonlinear terms, we have a richer basis set, which offers appreciable improvement.
The ideal performance of digital predistortion certainly relies on robust predistorters that can completely compensate for the nonlinearities of the power amplifier. In reality, however, the performance can also be affected by the analog imperfections in the transmitter, which are introduced by the analog components; mostly analog filters and quadrature modulators. There are two common configurations for the upconversion chain in the transmitter:
two-stage upconversion and direct upconversion. For a two-stage upconversion transmitter, we design a band-limited equalizer to compensate for the frequency response of the surface acoustic wave (SAW) filter which is usually employed in the IF stage. For a direct upconversion transmitter, we develop a model to describe the frequency-dependent gain/phase imbalance and dc offset. We then develop two methods to construct compensators for the imbalance and dc offset. These compensation techniques help to correct for the analog imperfections, which in turn improve the overall predistortion performance.
