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Performance Analysis and Coefficient Generation Method of Parallel Hammerstein Model Under Underdetermined Condition.

Nanzhou Hu1, Youyang Xiang1, Mingyang Li1

  • 1Institute of Electronic Engineering, China Academy of Engineering Physics, Mianyang 621999, China.

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Summary
This summary is machine-generated.

This study analyzes the parallel Hammerstein (PH) model for nonlinear systems. A novel method using singular value decomposition (SVD) and least squares (LS) simplifies coefficient estimation and improves performance.

Keywords:
coefficients estimationmemory polynomial modelnonlinearityparallel Hammerstein modelsingular value decomposition

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Nonlinear System Modeling

Background:

  • Nonlinear signal models are crucial for power amplifier predistortion and self-interference cancellation.
  • The parallel Hammerstein (PH) model, while effective, presents challenges in performance analysis and coefficient estimation due to its hybrid architecture.
  • Understanding and optimizing PH model performance is vital for advanced wireless communication systems.

Purpose of the Study:

  • To analyze the performance of the parallel Hammerstein (PH) model in nonlinear systems with memory effects.
  • To develop an efficient coefficient estimation method for the PH model.
  • To compare the PH model's performance with the memory polynomial (MP) model.

Main Methods:

  • Comparative analysis of PH and memory polynomial (MP) models using identical basis functions.
  • Performance evaluation across varying parallel branches, nonlinear orders, and memory depths.
  • Derivation of a closed-form performance expression for the PH model under underdetermined conditions using singular value decomposition (SVD).
  • Development of a coefficient generation method combining SVD and least squares (LS).

Main Results:

  • A closed-form expression for PH model performance was derived, linking it to the singular values of the MP model's coefficient matrix.
  • The proposed SVD-LS method enables direct coefficient computation and real-time performance assessment.
  • Simulations demonstrated that selecting parallel branches corresponding to larger singular values yields near-optimal performance with reduced complexity.

Conclusions:

  • The SVD-LS method effectively addresses PH model coefficient estimation and performance analysis challenges.
  • Optimizing parallel branch selection based on singular values is key to achieving high performance and efficiency in PH models.
  • This research provides a valuable framework for designing and implementing advanced nonlinear signal processing techniques.