Generalized MLP/BP-based MIMO DFEs for overcoming ISI and ACI in band-limited channels

Abstract

in this work, we base on generalized multi-layered perceptron neural networks with backpropagation algorithm (Generalized MLP/BP) to construct multi-input multi-output (MIMO) decision feedback equalizers (DFEs). The proposal is used to recover distorted nonretum-to-zero (NRZ) data in wireline parallel bandlimited channels. From the simulations, we note that the proposed design can recover severe distorted NRZ data as well as suppress intersymbol interference (ISI), adjacent channel interference (ACI) and background noise. The better BER performance as compared to a set of LMS DFEs and an MLPIBP-based MIMO DFE is achieved in the wireline parallel band-limited channels where the data rate is ten times as much as the channel bandwidth.