Equalization of Stanford University Interim channels using adaptive multilayer perceptron NN model

This paper presents adaptive channel equalization for six standard Stanford University Interim (SUI) channels using Least Mean Square Algorithm (LMS) and Multilayer Perceptron Algorithm (MLP) models. The performance analysis of the adaptive equalizers was done based on the Bit Error Rate (BER). The performance of LMS algorithm is found decent whenever there is no nonlinearity in system, whereas in presence of nonlinearity in the system, the LMS algorithm fails to perform well. Under such a case, the MLP based equalizer is found to be better alternative. In simulation analysis, BPSK signal are transmitted through various SUI channels. The results were compared and it was found that under nonlinear conditions, MLP algorithm gives better BER in comparison to LMS.