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A Low-Computational-Complexity Digital Predistortion Model for Wideband Power Amplifier.

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Summary

A new Composition Piecewise Memory Polynomial (CPMP) model enhances power amplifier (PA) linearization by segmenting signals. This digital predistortion technique significantly reduces nonlinearity and memory effects, improving adjacent channel power ratio (ACPR).

Keywords:
complexitycomposition piecewise memory polynomialdigital predistortionlinearizationwideband power amplifier

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Telecommunications

Background:

  • Wideband power amplifiers (PAs) suffer from severe nonlinearity and strong memory effects, degrading signal quality.
  • Existing digital predistortion models struggle to efficiently address these complex PA characteristics.

Purpose of the Study:

  • To propose a novel Composition Piecewise Memory Polynomial (CPMP) digital predistortion model.
  • To improve linearization performance and reduce computational complexity in wideband PAs.

Main Methods:

  • Developed a CPMP model based on a Vector Switched (VS) behavioral model.
  • Segmented input signal envelope values into three segments based on PA nonlinear distortion characteristics.
  • Employed different polynomial models (GMP, MP, higher-order GMP) for each segment, sharing a fundamental MP.
  • Configured distinct cross terms, memory depths, and polynomial orders for each segment.

Main Results:

  • Achieved a significant improvement in adjacent channel power ratio (ACPR) from -36 dBc to -54 dBc.
  • Performance matched the Generalized Memory Polynomial (GMP) model and surpassed Piecewise Dynamic Deviation Reduction (PDDR) and Decomposed Vector Rotation (DVR) models by 0.5 dBc.
  • Reduced computational complexity for parameter extraction to 28.8% (DVR), 21.79% (GMP), and 12.83% (PDDR).

Conclusions:

  • The proposed CPMP model effectively linearizes wideband PAs with severe nonlinearity and memory effects.
  • The segmented approach offers superior linearization performance with substantially reduced computational complexity.
  • This model presents a promising solution for advanced digital predistortion in modern communication systems.