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

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3 Author(s)
Yu-Jhih Wu ; Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA ; P. M. Chau ; R. Hecht-Nielsen

A supervised learning neural network (SLNN) coprocessor which enhances the performance of a digital soft-decision Viterbi decoder used for forward error correction in a digital communication channel with either fading plus additive white Gaussian noise (AWGN) or pure AWGN has been investigated and designed. The SLNN is designed to cooperate with a phase shift keying (PSK) demodulator, an automatic gain control (AGC) circuit, and a 3-bit quantizer which is an analog to digital convertor. It is trained to learn the best uniform quantization step-size Δ BEST as a function of the mean and the standard deviation of various sets of Gaussian distributed random variables. The channel cutoff rate (R0) of the channel is employed to determine the best quantization threshold step-size (ΔBEST) that results in the minimization of the Viterbi decoder output bit error rate (BER). For a digital communication system with a SLNN coprocessor, consistent and substantial BER performance improvements are observed. The performance improvement ranges from a minimum of 9% to a maximum of 25% for a pure AWGN channel and from a minimum of 25% to a maximum of 70% for a fading channel. This neural network coprocessor approach can be generalized and applied to any digital signal processing system to decrease the performance losses associated with quantization and/or signal instability

Published in:

IEEE Transactions on Neural Networks  (Volume:6 ,  Issue: 4 )