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Equalization of Stanford University Interim channels using adaptive multilayer perceptron NN model
Lavania S, Kumam B, Matey P.S, Annepu V,
Published in IEEE
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. © 2015 IEEE.
About the journal
JournalData powered by Typeset2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)
PublisherData powered by TypesetIEEE
Open Access0