Open AccessAn Efficient ANN Interference Cancelation for High Order Modulation over Rayleigh Fading Channel
Author(s) -
Fateh Bouguerra,
Lamir Saidi
Publication year - 2018
Publication title -
journal of telecommunications and information technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.151
H-Index - 12
eISSN - 1899-8852
pISSN - 1509-4553
DOI - 10.26636/jtit.2018.125718
Subject(s) - quadrature amplitude modulation , computer science , qam , bit error rate , rayleigh fading , multilayer perceptron , adaptive equalizer , radial basis function , modulation (music) , mean squared error , artificial neural network , electronic engineering , algorithm , link adaptation , interference (communication) , channel (broadcasting) , fading , telecommunications , equalizer , artificial intelligence , mathematics , statistics , engineering , acoustics , physics
High order modulation (HOM) presents a key challenge in increasing spectrum eciency in 4G and upcoming 5G communication systems. In this paper, two non-linear adaptive equalizer techniques based on multilayer perceptron (MLP) and radial basis function (RBF) are designed and applied on HOM to optimize its performance despite its high sensitivity to noise and channel distortions. The articial neural network’s (ANN) adaptive equalizer architectures and learning methods are simplied to avoid more complexity and to ensure greater speed in symbol decision making. They will be compared with the following popular adaptive lters: least mean square (LMS) and recursive least squares (RLS), in terms of bit error rate (BER) and minimum square error (MSE) with 16, 64, 128, 256, 512 and 1024 quadrature amplitude modulation (QAM). By that, this work will show the advantage that the MLP equalizer has, in most cases, over RBF and traditional linear equalizers.