Equalisation of a wireless ATM channel using a pruned recurrent neural network
by Dong-Chul Park
International Journal of Systems, Control and Communications (IJSCC), Vol. 2, No. 4, 2010

Abstract: A equalisation method of a wireless Asynchronous Transfer Mode (ATM) communication channel using a Complex BiLinear Recurrent Neural Network (CBLRNN) is proposed in this paper. A Genetic Algorithm (GA) is used for the pruning process of the trained CBLRNN. As a result, a pruned Bilinear Recurrent Neural Network (BLRNN) is obtained and the pruned BLRNN can reduce the computational cost by 29.9% in terms of the number of weights. The equaliser based on CBLRNN pruned by the GA is compared with Decision Feedback Equaliser (DFE), Volterra filter based equaliser, and Multilayer Perceptron Neural Network Equaliser. Experiments show that the pruned CBLRNN equaliser for 8PSK signals gives favourable results in the Symbol Error Rate (SER) criterion over conventional equalisers.

Online publication date: Thu, 30-Sep-2010

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Systems, Control and Communications (IJSCC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com