MLP/BP-based MIMO DFEs for Suppressing ISI and ACI to Recover Distorted QPSK Signal in Severe ISI Channels

Abstract

In this work, we base oil multi-layered perceptron neural networks with backpropagation algorithm (MLP/BP) to construct multi-input multi-output (MIMO) decision feedback equalizers (DFEs). The proposal is used to recover distorted quadrature phase-shift keying (QPSK) signal. From the simulations, we note that the proposed scheme can recover severe distorted signals as well as suppress intersymbol interference (ISI), adjacent channel interference (ACI) and background additive white Gaussian noise (AWGN). The better bit-error-rate (BER) and packet-error-rate (PER) performance as compared to a set of least-mean-square (LMS) DFEs is achieved. The presented MLP/BP-based MIMO DFE under the severe ISI channels with ACI and AWGN can improve over 1.5dB at PER=10(-1) and BER=10(-3).