Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/342765
Type: Artigo
Title: Blind fuzzy adaptation step control for a concurrent neural network equalizer
Author: Mayer, Kayol S.
De Oliveira, Matheus S.
Mueller, Candice
De Castro, Fernando C. C.
De Castro, Maria C. F.
Abstract: Mobile communications, not infrequently, are disrupted by multipath propagation in the wireless channel. In this context, this paper proposes a new blind concurrent equalization approach that combines a Phase Transmittance Radial Basis Function Neural Network (PTRBFNN) and the classic Constant Modulus Algorithm (CMA) in a concurrent architecture, with a Fuzzy Controller (FC) responsible for adapting the PTRBFNN and CMA step sizes. Differently from the Neural Network (NN) based equalizers present in literature, the proposed Fuzzy Controller Concurrent Neural Network Equalizer (FC-CNNE) is a completely self-taught concurrent architecture that does not need any training. The Fuzzy Controller inputs are based on the estimated mean squared error of the equalization process and on its variation in time. The proposed solution has been evaluated over standard multipath VHF/UHF channels defined by the International Telecommunication Union. Results show that the FC-CNNE is able to achieve lower residual steady-state MSE value and/or faster convergence rate and consequently lower Bit Error Rate (BER) when compared to Constant Modulus Algorithm-Phase Transmittance Radial Basis Function Neural Network (CMA-PTRBFNN) equalizer
Subject: Controle ótimo fuzzy
Country: Reino Unido
Editor: Hindawi
Rights: Aberto
Identifier DOI: 10.1155/2019/9082362
Address: https://www.hindawi.com/journals/wcmc/2019/9082362/
Date Issue: 2019
Appears in Collections:FEEC - Artigos e Outros Documentos

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