Vancouver, BC
June 26, 2011
June 26, 2011
June 29, 2011
2153-5965
Electrical and Computer
22
22.1594.1 - 22.1594.22
10.18260/1-2--18554
https://strategy.asee.org/18554
493
Robert Santucci is an electrical engineering Ph.D. student at Arizona State University researching the use of digital signal processing techniques for power amplifier linearization in wireless communications systems.
Andreas Spanias is Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University (ASU). He is also the founder and director of the SenSIP center and industry consortium (NSF I/UCRC). His research interests are in the areas of adaptive signal processing, speech processing, and audio sensing. He and his student team developed the computer simulation software Java-DSP (J-DSP - ISBN 0-9724984-0-0). He is author of two text books: Audio Processing and Coding by Wiley and DSP; An Interactive Approach. He served as Associate Editor of the IEEE Transactions on Signal Processing and as General Co-chair of IEEE ICASSP-99. He also served as the IEEE Signal Processing Vice-President for Conferences. Andreas Spanias is co-recipient of the 2002 IEEE Donald G. Fink paper prize award and was elected Fellow of the IEEE in 2003. He served as Distinguished lecturer for the IEEE Signal processing society in 2004.
Usage of Java-DSP to Demonstrate Power Amplifier Linearization Techniques J-DSP is a Java-based object-oriented programming environment developed from theground up for education and research by our university. This paper builds upon our previouswork1 by presenting a more detailed treatment of power amplifier linearization techniques. Thetutorials developed are intended to familiarize digital signal processing (DSP) students with themixed-signal application of DSP in coordination with a radio transmitter’s power amplifier. Themodule is intended to provide students a visual method of seeing not just the results ofpredistortion as demonstrated last year but also a visual indication of the internal state of thepredistorter. It also presents an alternate method of implementing linearization by fitting theinverse function using a system identification neural network. For each technique, a set oflessons is implemented. The first technique is the gain-based predistorter2. The previous tutorials demonstratedthe gain compression present in power amplifiers, and the resulting harmonics introduced in thespectrum. It also demonstrated how a well designed gain-based look-up-table can be configuredto fix the distortion, and students could run different configurations and see the improvement. Inthis iteration of the tutorials, the gain-based LUT has been expanded to show the internal gainsof the predistorter bins, the nominal power amplifier gain within a given bin, the histogram ofpoints lying within a bin for given modulation scheme, and the net linearized system gain withineach given bin. As before, spectrum for both linearized and nominal cases is shown. These newfeatures allow linking those spectral changes back to the behavior within the predistorter itself. The second technique new to this tutorial is the artificial neural network basedpredistortion. In this technique with a memoryless power amplifier model, a neural network istrained to identify the inverse function of the power amplifier with nominal gain removed. Thetutorial demonstrates the use of multilayer perceptrons. Similar to the prior demonstrations, theneural network output is the spectrum with and without predistortion. Finally, these techniques will be used at the end of an undergraduate digital signalprocessing class. Students will be evaluated before and after completion of the tutorials todemonstrate the change in their proficiency with these basic communications concepts.References:[1] ASEE Paper. Author names withheld.[2] Cavers, J.K.; , "A linearizing predistorter with fast adaptation," IEEE Vehicular TechnologyConference, pp.41-47, 6-9 May 1990.
Santucci, R., & Spanias, A. S. (2011, June), Use of Java-DSP to Demonstrate Power Amplifier Linearization Techniques Paper presented at 2011 ASEE Annual Conference & Exposition, Vancouver, BC. 10.18260/1-2--18554
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