Adaptive channel equalization using recurrent neural network under SUI channel model

TLDR: This paper investigates Neural Networks based adaptive channel equalization for standard Stanford University Interim (SUI) channels and shows that the RNN equalizer consistently outperform the MLP equalizer by giving better BER.

Abstract: This paper investigates Neural Networks (NNs) based adaptive channel equalization for standard Stanford University Interim (SUI) channels. The NN models like Multilayer Perceptron Algorithm (MLP) and Recurrent Neural Network (RNN) are used to design adaptive equalizers. The Back Propagation (BP) and Real Time recurrent Learning (RTRL) are used for training MLP and RNN models respectively. As NNs are known for highly non-linear structure, these models are better suitable for equalization of system with high non-linearity. The performance of RNN is compared with MLP in terms of Bit Error Rate (BER). In simulation analysis, BPSK signal are transmitted through various SUI channels, which are modeled for fixed wireless applications. The simulation results illustrates that the RNN equalizer consistently outperform the MLP equalizer by giving better BER.