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A recurrent neural network for adaptive beamforming and array correction.

Hangjun Che1, Chuandong Li1, Xing He1

  • 1School of Electronics and Information Engineering, Southwest University, Chongqing 400715, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 21, 2016
PubMed
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A novel recurrent neural network (RNN) effectively solves adaptive beamforming problems by minimizing interference. This stable algorithm converges to optimal solutions, even with array mismatches and large constraints.

Area of Science:

  • Signal Processing
  • Artificial Intelligence
  • Optimization

Background:

  • Adaptive beamforming is crucial for minimizing interference in signal processing.
  • Existing methods face challenges with large-scale constraints and array mismatches.
  • Convex optimization offers a framework for beamforming but requires efficient solvers.

Purpose of the Study:

  • To propose a recurrent neural network (RNN) for adaptive beamforming.
  • To address the challenge of minimizing sidelobe interference.
  • To demonstrate the algorithm's stability and convergence properties.

Main Methods:

  • Formulated the adaptive beamforming problem as a convex optimization problem.
  • Designed an RNN to optimize system weight values within a feasible region.
Keywords:
Array stateBeamforming optimizationExact solutionsRecurrent neural network

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  • Utilized array state and plane wave information to define the feasible region.
  • Proved the algorithm's stability and convergence using Lyapunov stability theory.

    • Main Results:

    • The proposed RNN algorithm demonstrates stability and converges to optimal solutions.
    • Simulations show the RNN's effectiveness in beamforming under array mismatch conditions.
    • The RNN exhibits a strong ability to find exact solutions for large-scale constrained problems.

    Conclusions:

    • The recurrent neural network is a viable and effective approach for adaptive beamforming.
    • The RNN offers superior performance compared to other optimization algorithms, especially under challenging conditions.
    • This work advances the application of neural networks in solving complex signal processing optimization problems.