Optimizing satellite broadcast scheduling problem using the competitive Hopfield neural network

Yu J. Shen, Ming-Shi Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In this paper the competitive Hopfield neural network method for finding a broadcasting schedule in the satellite system will be described. The Satellite Broadcast Scheduling (SBS) Problem is known as an NP-complete problem. Communication links between satellites and ground terminals are provided in a repetition of time slots. The goal of the proposed algorithm is to find the broadcasting schedule of satellites with the maximum number of broadcasting time slots under the constraints. A competitive learning rule provides a highly effective means for obtaining a resonance solution and is capable of reducing the time-consuming effort to obtain coefficients. The proposed method can always satisfy the problem constraints and guarantee the viability of the solutions for the SBS problem. The competitive mechanism simplifies the network complexity. The proposed method is greatly suitable for implementation on a digital machine. Furthermore, the competitive Hopfield neural network method permits temporary energy increases to escape from local minima. Simulation results show that the competitive Hopfield neural network method can improve system performance and with fast convergence and high reliability.

Original languageEnglish
Title of host publication2007 Wireless Telecommunications Symposium, WTS 2007
DOIs
Publication statusPublished - 2007
Event2007 Wireless Telecommunications Symposium, WTS 2007 - Pomona, CA, United States
Duration: 2007 Apr 262007 Apr 28

Other

Other2007 Wireless Telecommunications Symposium, WTS 2007
CountryUnited States
CityPomona, CA
Period07-04-2607-04-28

Fingerprint

Hopfield neural networks
broadcast
neural network
scheduling
Scheduling
Satellites
broadcasting
Broadcasting
Telecommunication links
guarantee
Computational complexity
energy
simulation
communication
learning
performance
time

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Communication

Cite this

Shen, Yu J. ; Wang, Ming-Shi. / Optimizing satellite broadcast scheduling problem using the competitive Hopfield neural network. 2007 Wireless Telecommunications Symposium, WTS 2007. 2007.
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abstract = "In this paper the competitive Hopfield neural network method for finding a broadcasting schedule in the satellite system will be described. The Satellite Broadcast Scheduling (SBS) Problem is known as an NP-complete problem. Communication links between satellites and ground terminals are provided in a repetition of time slots. The goal of the proposed algorithm is to find the broadcasting schedule of satellites with the maximum number of broadcasting time slots under the constraints. A competitive learning rule provides a highly effective means for obtaining a resonance solution and is capable of reducing the time-consuming effort to obtain coefficients. The proposed method can always satisfy the problem constraints and guarantee the viability of the solutions for the SBS problem. The competitive mechanism simplifies the network complexity. The proposed method is greatly suitable for implementation on a digital machine. Furthermore, the competitive Hopfield neural network method permits temporary energy increases to escape from local minima. Simulation results show that the competitive Hopfield neural network method can improve system performance and with fast convergence and high reliability.",
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Shen, YJ & Wang, M-S 2007, Optimizing satellite broadcast scheduling problem using the competitive Hopfield neural network. in 2007 Wireless Telecommunications Symposium, WTS 2007., 4563323, 2007 Wireless Telecommunications Symposium, WTS 2007, Pomona, CA, United States, 07-04-26. https://doi.org/10.1109/WTS.2007.4563323

Optimizing satellite broadcast scheduling problem using the competitive Hopfield neural network. / Shen, Yu J.; Wang, Ming-Shi.

2007 Wireless Telecommunications Symposium, WTS 2007. 2007. 4563323.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - In this paper the competitive Hopfield neural network method for finding a broadcasting schedule in the satellite system will be described. The Satellite Broadcast Scheduling (SBS) Problem is known as an NP-complete problem. Communication links between satellites and ground terminals are provided in a repetition of time slots. The goal of the proposed algorithm is to find the broadcasting schedule of satellites with the maximum number of broadcasting time slots under the constraints. A competitive learning rule provides a highly effective means for obtaining a resonance solution and is capable of reducing the time-consuming effort to obtain coefficients. The proposed method can always satisfy the problem constraints and guarantee the viability of the solutions for the SBS problem. The competitive mechanism simplifies the network complexity. The proposed method is greatly suitable for implementation on a digital machine. Furthermore, the competitive Hopfield neural network method permits temporary energy increases to escape from local minima. Simulation results show that the competitive Hopfield neural network method can improve system performance and with fast convergence and high reliability.

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