A Low-Complexity Digital Predistortion Algorithm for Power Amplifier Linearization

Transmission signals of modern communication systems, such as orthogonal frequency-division multiplexing signals, usually have large peak-to-average power ratio. These signals are sensitive to the power amplifier's nonlinearity, which generates both in-band distortion and out-of-band spectral r...

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Vydané v:IEEE transactions on broadcasting Ročník 60; číslo 4; s. 670 - 678
Hlavní autori: Qian, Hua, Yao, Saijie, Huang, Hao, Feng, Weifeng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.12.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adaptation models
adaptive digital predistortion
Adaptive systems
Algorithms
Basis functions
Communication systems
Complexity
Computation
Computational complexity
Digital
Least mean squares algorithm
Least squares approximations
Linearization
low-complexity
Mathematical models
orthonormal basis functions
Power amplifiers
Predistortion
Quantization (signal)
adaptive digital predistortion
low-complexity
Power amplifiers
orthonormal basis functions
ISSN:0018-9316, 1557-9611
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Popis
Shrnutí:Transmission signals of modern communication systems, such as orthogonal frequency-division multiplexing signals, usually have large peak-to-average power ratio. These signals are sensitive to the power amplifier's nonlinearity, which generates both in-band distortion and out-of-band spectral regrowth. The adaptive digital predistortion (DPD) is an efficient linearization technique without sacrificing the power efficiency. To estimate the DPD coefficients, numerical instability and computational complexity are bottlenecks. In this paper, we propose a general approach to derive orthonormal basis functions, which can improve the numerical stability during the coefficients estimation. By applying the orthonormal basis functions, we further propose an adaptive algorithm that exhibits a low computation complexity of least mean squares algorithm while retaining the fast convergence speed of recursive least squares algorithm. Both simulation and experimental results validate the effectiveness of the proposed algorithm.
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ISSN:0018-9316
1557-9611
DOI:10.1109/TBC.2014.2352911