Autonomous and Decentralized Optimization of Large-Scale Heterogeneous Wireless Networks by Neural Network Dynamics

Ha Nguyen TRAN
Yoshitoshi MURATA
Hiroshi HARADA
Shuzo KATO

IEICE TRANSACTIONS on Communications   Vol.E91-B    No.1    pp.110-118
Publication Date: 2008/01/01
Online ISSN: 1745-1345
DOI: 10.1093/ietcom/e91-b.1.110
Print ISSN: 0916-8516
Type of Manuscript: Special Section PAPER (Special Section on Cognitive Radio and Spectrum Sharing Technology)
Category: Distributed Optimization
cognitive radio,  heterogeneous wireless networks,  neural networks,  radio resource management,  radio access technology selection,  

Full Text: PDF(809.8KB)>>
Buy this Article

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.