Cart (Loading....) | Create Account
Close category search window

A maximum channel reuse scheme with Hopfield Neural Network based static cellular radio channel allocation systems

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
$31 $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

3 Author(s)
Jie-Hung Lee ; Grad. Inst. of Comput. & Commun. Eng., Nat. Taipei Univ. of Technol., Taipei ; Chiu-Ching Tuan ; Tzung-Pei Hong

In recent years, wireless and mobile communication systems become increasingly popular. The demand for mobile communication has thus made the industry put more efforts towards designing new-generation systems. One of the important issues in mobile-phone communications is about the static channel assignment problem (SCAP). Although many techniques have been proposed for SCAP, a challenge for the cellular radio communication system is how to enhance and maximize the frequency reuse. The general SCAP is known as an NP-hard problem. The static channel assignment scheme based on the Hopfield neural network was shown to perform well when compared to some other schemes such as graph coloring and genetic algorithm (GA). In this paper, we extend Kim et al.psilas modified Hopfield neural network methods and focus on channel reusing to obtain a near-optimum solution for CAP. Several constraints are considered for obtaining the desired results. Eight-benchmark problems are simulated and the energy evolution process is discussed. Simulation results demonstrated that the proposed scheme could make higher channel reuse rate.

Published in:

Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on

Date of Conference:

1-8 June 2008

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.