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A supervised learning neural network coprocessor for soft-decision maximum-likelihood decoding.

Y J Wu1, P M Chau, R Hecht-Nielsen

  • 1Dept. of Electr. and Comput. Eng., California Univ., San Diego, La Jolla, CA.

IEEE Transactions on Neural Networks
|January 1, 1995
PubMed
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A novel supervised learning neural network (SLNN) coprocessor significantly improves Viterbi decoder performance in digital communication systems. This enhancement reduces bit error rates (BER) by up to 70% in fading channels and 25% in additive white Gaussian noise (AWGN) channels.

Area of Science:

  • Digital Communications
  • Artificial Intelligence
  • Signal Processing

Background:

  • Digital communication systems rely on forward error correction (FEC) techniques like the Viterbi decoder.
  • Quantization and signal instability introduce performance losses in digital signal processing.
  • Additive white Gaussian noise (AWGN) and fading channels degrade signal quality.

Purpose of the Study:

  • To design and investigate a supervised learning neural network (SLNN) coprocessor.
  • To enhance the performance of a digital soft-decision Viterbi decoder.
  • To minimize bit error rate (BER) in digital communication channels.

Main Methods:

  • Developed an SLNN coprocessor integrated with PSK demodulator, AGC, and a 3-bit quantizer.
  • Trained the SLNN to determine the optimal uniform quantization step-size (Delta BEST).

Related Experiment Videos

  • Utilized channel cutoff rate (R(0)) to find Delta BEST minimizing BER.

    • Main Results:

    • Achieved substantial BER performance improvements using the SLNN coprocessor.
    • Observed 9-25% BER improvement in pure AWGN channels.
    • Observed 25-70% BER improvement in fading channels.

    Conclusions:

    • The SLNN coprocessor effectively enhances Viterbi decoder performance.
    • This approach significantly reduces BER in challenging communication environments.
    • The SLNN coprocessor concept is generalizable to other digital signal processing systems.