Autonomous and decentralized optimization of large-scale heterogeneous wireless networks by neural network dynamics

Mikio Hasegawa, Ha Nguyen Tran, Goh Miyamoto, Yoshitoshi Murata, Hiroshi Harada, Shuzo Kato

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


We propose a neurodynamical approach to a large-scale optimization problem in Cognitive Wireless Clouds, in which a huge number of mobile terminals with multiple different air interfaces autonomously utilize the most appropriate infrastructure wireless networks, by sensing available wireless networks, selecting the most appropriate one, and reconfiguring themselves with seamless handover to the target networks. To deal with such a cognitive radio network, game theory has been applied in order to analyze the stability of the dynamical systems consisting of the mobile terminals' distributed behaviors, but it is not a tool for globally optimizing the state of the network. As a natural optimization dynamical system model suitable for large-scale complex systems, we introduce the neural network dynamics which converges to an optimal state since its property is to continually decrease its energy function. In this paper, we apply such neurodynamics to the optimization problem of radio access technology selection. We compose a neural network that solves the problem, and we show that it is possible to improve total average throughput simply by using distributed and autonomous neuron updates on the terminal side.

Original languageEnglish
Pages (from-to)110-118
Number of pages9
JournalIEICE Transactions on Communications
Issue number1
Publication statusPublished - 2008 Jan 1


  • Cognitive radio
  • Heterogeneous wireless networks
  • Neural networks
  • Radio access technology selection
  • Radio resource management

ASJC Scopus subject areas

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


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