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

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 (Δ_BEST) 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