A two-phase chaotic neural network algorithm for channel assignment in cellular systems

  • Authors:

  • Tinggao Qin;Xiaojin Zhu;Yanchun Chen;Jian Wang

  • Affiliations:

  • School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, P. R. China;School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, P. R. China;School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, P. R. China;School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, P. R. China

  • Venue:
  • LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
  • Year:
  • 2007

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Abstract

In this paper, a new chaotic neural network algorithm for channel assignment in cellular networks is proposed as an optimization algorithm, and used in CAP. There are two phases in the new model. According to the difficulty measure of each cell, the first phase is executed to assign channels cell by using the frequency exhaustive strategy. If the optimum assignment solution is not obtained in the first stage, another phase which generated by adding chaotic noise to each neuron of the Hopfield neural network (HNN) called DCN-HNN is taken, and the DCN-HNN stage is then applied to continue the channel assignment until the optimum assignment is made or a maximum number of iteration is reached. The Kunz benchmark test problem is also used to compare the performance of the new algorithm against other algorithms. Simulation results show that the new method has a higher searching ability and lower computing time in searching the global minimum.