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Robust CDMA multiuser detection using a neural-network approach.

Teong Chee Chuah1, B S Sharif, O R Hinton

  • 1Dept. of Electr. and Electron. Eng., Newcastle upon Tyne Univ., UK.

IEEE Transactions on Neural Networks
|February 5, 2008
PubMed
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This study introduces a recurrent neural network (RNN) to implement robust linear decorrelating detectors (LDD) and M-decorrelating detectors (MDD) without matrix inversion. Simulations show improved performance, especially with impulsive noise using alpha-stable distributions.

Area of Science:

  • Signal Processing
  • Machine Learning
  • Neural Networks

Background:

  • A robust linear decorrelating detector (LDD) using Huber's M-estimation was recently proposed.
  • Implementing LDD typically requires matrix inversion, which can be computationally intensive.

Purpose of the Study:

  • To implement LDD and M-decorrelating detectors (MDD) using a three-layer recurrent neural network (RNN) without matrix inversion.
  • To demonstrate computational savings and performance gains, particularly in impulsive noise environments.

Main Methods:

  • Utilized a three-layer RNN to iteratively minimize a computational energy function for LDD implementation.
  • Incorporated sigmoidal neurons in the RNN for MDD implementation.
  • Modeled impulsive noise using non-Gaussian alpha-stable distributions and employed the geometric signal-to-noise ratio (G-SNR).

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Main Results:

  • Successfully implemented LDD and MDD using RNNs, eliminating the need for matrix inversion.
  • Demonstrated significant computational savings in realistic network scenarios.
  • Achieved further performance enhancements for subspace-based blind MDD by using robust initial estimates.

Conclusions:

  • Recurrent neural networks offer an efficient alternative for implementing robust decorrelating detectors.
  • The proposed RNN-based detectors show promise for signal processing in non-Gaussian noise environments.
  • The method provides a computationally advantageous approach to robust signal detection.