J-DSP is a Java-based object-oriented programming environment developed from the ground up for education and research by Arizona State University. This paper builds upon our previous work1 by developing several new and advanced functions that support a more detailed treatment of power amplifier linearization techniques. The tutorials developed are intended to familiarize students with the mixed-signal application of digital signal processing (DSP) in coordination with a radio transmitter's power amplifier. Specifically, the new functions illustrate the effects of design tradeoffs between DSP and circuits while masking the complexity of the implementations for senior undergraduate students. This is accomplished by plotting the internal state of the predistorter and producing two metrics, adjacent channel power ratio (ACPR) and error vector magnitude (EVM), that quantify absolute performance for a given simulation. Two different techniques for amplifier linearization are presented, and a tutorial has been developed for each technique. The first technique is the gain-based predistorter. The previous tutorials1 demonstrated the gain compression present in power amplifiers, and the resulting harmonics introduced in the output spectrum. It also demonstrated how a well designed gain-based look-up-table (LUT) can be configured to fix the distortion, and students could run different configurations and see the improvement. In this iteration of the tutorials, the gain-based LUT has been expanded to show the internal gains of the predistorter bins, the nominal power amplifier gain within a given bin, the histogram of points lying within a bin for given modulation scheme, and the net linearized system gain within each given bin. As before, spectra for both linearized and nominal cases are shown. These new features allow demonstration of internal effects and quantify the performance differences created by design choices in the look-up-table. The second technique, completely new to our research, is the artificial neural network based predistortion. In this technique, a neural network is trained to identify the inverse function of the power amplifier with desired gain removed. As output, the simulations yield ACPR, EVM, spectrum with and without predistortion, and the internal gains of the predistorter, linearized power amplifier system, and the nominal power amplifier.
|Original language||English (US)|
|Journal||ASEE Annual Conference and Exposition, Conference Proceedings|
|State||Published - 2011|
ASJC Scopus subject areas