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  • Research Article
  • Open Access

A New OFDMA Scheduler for Delay-Sensitive Traffic Based on Hopfield Neural Networks

EURASIP Journal on Wireless Communications and Networking20082008:817676

  • Received: 1 May 2007
  • Accepted: 4 January 2008
  • Published:


This paper introduces a novel joint channel and queuing-aware OFDMA scheduler for delay-sensitive traffic based on a hopfield neural network (HNN) scheme. It allows providing an optimum OFDMA performance by solving a complex combinational problem. The algorithm is based on distributing the available subcarriers among the users depending, on the one hand, on the time left for the transmission of the different packets in due time, so that packet droppings are avoided. On the other hand, it also accounts for the available channel capacity in each subcarrier depending on the channel status reported by the different users. The different requirements are captured in the form of an energy function that is minimized by the algorithm. In that respect, the paper illustrates two different algorithms coming from two settings of this energy function. The algorithms have been evaluated for delay-sensitive traffic and they have been compared against other state-of-the-art algorithms existing in the literature, exhibiting a better behavior in terms of packet-dropping probability.


  • Neural Network
  • Information System
  • Energy Function
  • System Application
  • Channel Status

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Authors’ Affiliations

Grup de Recerca en Tecnologies i Estratègies de les Telecomunicacions, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Passeig de Circumval.lació 8, Barcelona, 08003, Spain
Grup de Recerca en Comunicacions Mòbils, Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya, C/ Jordi Girona 31, Barcelona, 08034, Spain


© Nuria García et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.