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

doi:10.1093/ietcom/e91-b.1.110

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

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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.

Publication: IEICE TRANSACTIONS on Communications Vol.E91-B No.1 pp.110-118

Publication Date: 2008/01/01

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Online ISSN: 1745-1345

DOI: 10.1093/ietcom/e91-b.1.110

Type of Manuscript: Special Section PAPER (Special Section on Cognitive Radio and Spectrum Sharing Technology)

Category: Distributed Optimization

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Mikio HASEGAWA, Ha Nguyen TRAN, Goh MIYAMOTO, Yoshitoshi MURATA, Hiroshi HARADA, Shuzo KATO, "Autonomous and Decentralized Optimization of Large-Scale Heterogeneous Wireless Networks by Neural Network Dynamics" in IEICE TRANSACTIONS on Communications, vol. E91-B, no. 1, pp. 110-118, January 2008, doi: 10.1093/ietcom/e91-b.1.110.
Abstract: 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.
URL: https://globals.ieice.org/en_transactions/communications/10.1093/ietcom/e91-b.1.110/_p

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@ARTICLE{e91-b_1_110,
author={Mikio HASEGAWA, Ha Nguyen TRAN, Goh MIYAMOTO, Yoshitoshi MURATA, Hiroshi HARADA, Shuzo KATO, },
journal={IEICE TRANSACTIONS on Communications},
title={Autonomous and Decentralized Optimization of Large-Scale Heterogeneous Wireless Networks by Neural Network Dynamics},
year={2008},
volume={E91-B},
number={1},
pages={110-118},
abstract={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.},
keywords={},
doi={10.1093/ietcom/e91-b.1.110},
ISSN={1745-1345},
month={January},}

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TY - JOUR
TI - Autonomous and Decentralized Optimization of Large-Scale Heterogeneous Wireless Networks by Neural Network Dynamics
T2 - IEICE TRANSACTIONS on Communications
SP - 110
EP - 118
AU - Mikio HASEGAWA
AU - Ha Nguyen TRAN
AU - Goh MIYAMOTO
AU - Yoshitoshi MURATA
AU - Hiroshi HARADA
AU - Shuzo KATO
PY - 2008
DO - 10.1093/ietcom/e91-b.1.110
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E91-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2008
AB - 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.
ER -