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Use of Java-DSP to Demonstrate Power Amplifier Linearization Techniques

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Use of Java-DSP to Demonstrate Power Amplifier Linearization Techniques AC 2011-2321: USE OF JAVA-DSP TO DEMONSTRATE POWER AMPLI- FIER LINEARIZATION TECHNIQUES Robert Santucci, Arizona State University 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. Prof. Andreas S Spanias, Arizona State University, ECEE, SenSIP Center 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. Page 22.1594.1 Page c American Society for Engineering Education, 2011 Use of Java-DSP to Demonstrate Power Amplifier Linearization Techniques Abstract 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. Introduction Page 22.1594.2 Page In this paper we present two tutorials of new J-DSP functions and exercises that are intended to familiarize senior-level undergraduate digital signal processing (DSP) students with the mixed-signal processing techniques used in wireless transmitters2,3. We illustrate several design tradeoffs using simple block diagram simulations in J-DSP4,5,6 that expose undergraduate students to the concepts without having to cover the advanced hardware and algorithmic complexity of these designs. In presenting these mixed-signal examples, we demonstrate the interdependence between three generally separate senior-level courses: radio-frequency (RF) circuit design, DSP, and communications. This exposes students to the cross disciplinary tradeoffs that must be made over the course of a large design project. After completing the tutorials, students will be given an assessment to determine and justify tradeoffs in an initial system design. Below we give an introduction to the utility and applications of RF power amplifiers (PAs), and then we continue with a description of the predistortion issues. With the rapid deployment currently underway for smartphone technology, and new data intensive usage models, it has become critical for cellular phone carriers to get the highest possible data-rate through the relative fixed available bandwidths7. This has forced cellular phone providers to move away from traditional constant amplitude modulation schemes, such as those used in the Global System for Mobile Communications (GSM) phones, to modulation schemes that make more efficient use of available bandwidth by modulating both amplitude and phase to transmit more bits per symbol. As a result of using amplitude modulation, current cellular phones must utilize transmitters and receivers with strict linearity requirements throughout the dynamic range of the modulated signal8. In addition to transmitting more bits per symbol, wireless providers want to transmit these symbols faster. This allows less filtering between the symbols, which can cause spectral broadening. Additionally, in an uncontrolled wireless environment the wireless signal can take multiple different paths to the receiving antenna. The varying paths to the receive antenna can be either direct line-of-sight or can involve multiple reflections off buildings, mountains, or even walls within a building. These paths can each have different propagation delays, and this can cause problems when the symbol rate is very fast in time. One way around this is to transmit many symbols in parallel very slowly. Thus, if the duration of each signal is much longer than the difference in time between the longest and shortest path, the path delay effect is largely mitigated. The natural next question is how to transmit multiple symbols in parallel in a wireless communication system without using many independent transmitters and receivers. This can be accomplished in constructing the signal to be transmitted. The traditional approach has been to transmit symbols as a series in time that moves as fast as the available bandwidth and spectrum requirements allow. In this new approach the symbols are not allocated as time-series, but rather to different frequencies in the discrete-time frequency-domain. Then, the time-domain signal to transmit is generated by taking the Inverse Fast Fourier Transform (IFFT) of the discrete frequency spectrum. Because the different frequency tones are orthogonal, this technique is 22.1594.3 Page known as Orthogonal Frequency Domain Multiplexing (OFDM) 9. It allows very tight allocation of the transmitted frequency spectrum to get maximum bit-rate with the smallest spillover into adjacent frequencies and can avoid multipath effects, but it does have some downsides. One of these downsides is that after the IFFT is taken the phase of each independent modulated sinusoid combines in a nearly random manner, which causes the time-domain signal to have a wide 10 amplitude variation, or dynamic range . In a cellular transmitter, the power amplifier is often the largest consumer of power as it drives the high power signal out of the antenna, so its power efficiency has a significant effect on handset battery life. The transmitted signal can be made very linear by keeping the amplifier biased in such a manner that the radio frequency (RF) signal never takes the amplifier into a compression region. However, designs where the bias point remains stationary over a wide- range of output signal levels require a large amount of bias power. This bias power is not transmitted out of the antenna, but rather is dissipated as heat and “wasted” energy. By biasing the amplifier intentionally in a manner such that a portion of each RF cycle is spent in compression, the bias power requirement can be made lower and thus overall power efficiency of the amplifier can be increased. As a downside, this clipping induced by compression reduces the fundamental gain and introduces harmonics that must be filtered away. The level at which compression occurs within a given amplifier is most commonly a fixed amplitude. As a result of the fixed compression level, the fraction of an RF cycle spent in compression and consequently fundamental gain becomes a function of the amplitude of the signal being amplified8. For constant-amplitude modulation schemes, this change in fundamental gain with varying amplitudes was not a big cost as it did not alter the baseband signal dramatically. For non-constant amplitude modulation schemes, a tradeoff forms involving amplifier efficiency versus its constant gain, also known as linearity. This tradeoff, involving
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