Filter Lookup Table Method for Power Amplifier Linearization

This paper presents a new method of digital adaptive predistortion for linearization of power amplifiers (PAs) exhibiting memory effects. The predistorter (PD) device consists of a lookup table (LUT) gain followed by a codebook of filters addressed by the index of the LUT. The adaptation is derived...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2007-05, Vol.56 (3), p.1076-1087
Main Authors: Jardin, P., Baudoin, G.
Format: Article
Language:English
Subjects:
Adaptive systems
Algorithms
Amplification
Baseband
Baseband predistortion
Channels
Engineering Sciences
Filters
Gain
Learning
Linearity
Linearization
Lookup tables
Mathematical analysis
Memoryless systems
Methods
Nonlinear distortion
nonlinear distortions
Polynomials
power amplifier (PA)
Power amplifiers
Predistortion
Signal and Image processing
Table lookup
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Summary:This paper presents a new method of digital adaptive predistortion for linearization of power amplifiers (PAs) exhibiting memory effects. The predistorter (PD) device consists of a lookup table (LUT) gain followed by a codebook of filters addressed by the index of the LUT. The adaptation is derived from direct learning for the LUT gains and indirect learning for the filter coefficients. We compared our results with those of two reference methods: a simple LUT system (with direct learning) and a memory polynomial system (with indirect learning). The performances of the new approach lie between those of the two reference methods in terms of adjacent channel power regrowth and error vector magnitude. The LUT is the less complex of the three methods, but it is a memoryless system, and it cannot correct the memory effects in the PA. The memory polynomial PD is more powerful, but its complexity is very high. The new technique, due to the addition of filters to the LUT, has possibilities to compensate not only for the nonlinearity but also for the memory effects in the PA, and it is one order of magnitude less complex than the memory polynomial system
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2007.895566