By Topic

An improved recurrent neural network for M-PAM symbol detection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Hacioglu, K. ; Dept. of Electr. & Electron. Eng., Eastern Mediterranean Univ., Mersin, Turkey

In this paper, a fully connected recurrent neural network (RNN) is presented for the recovery of M-ary pulse amplitude modulated (M-PAM) signals in the presence of intersymbol interference and additive white Gaussian noise. The network makes use of two different activation functions. One is the traditional two-level sigmoid function, which is used at its hidden nodes, and the other is the M-level sigmoid function (MSF), which is used at the output node. The shape of the M-level activation function is controlled by two parameters: the slope and shifting parameters. The effect of these parameters on the learning performance is investigated through extensive simulations. In addition, the network is compared with a linear transversal equalizer, a decision feedback equalizer and a recently proposed RNN equalizer which has used a scaled sigmoid function (SSF) at its output node. Comparisons are made in terms of their learning properties and symbol error rates. It is demonstrated that the proposed RNN equalizer performs better, provided that the MSF parameters are properly selected

Published in:

Neural Networks, IEEE Transactions on  (Volume:8 ,  Issue: 3 )