Application of higher order neural network dynamics to distributed radio resource usage optimization of cognitive wireless networks

Authors:
Mikio Hasegawa;Taichi Takeda;Taro Kuroda;Ha Nguyen Tran;Goh Miyamoto;Yoshitoshi Murata;Hiroshi Harada;Shuzo Kato
Affiliations:
Tokyo University of Science, Tokyo, Japan and National Institute of Information and Communications Technology, Yokosuka, Japan;Tokyo University of Science, Tokyo, Japan;Tokyo University of Science, Tokyo, Japan;Tokyo University of Science, Tokyo, Japan;Tokyo University of Science, Tokyo, Japan;Iwate Prefectural University, Takizawa, Japan and National Institute of Information and Communications Technology, Yokosuka, Japan;National Institute of Information and Communications Technology, Yokosuka, Japan;National Institute of Information and Communications Technology, Yokosuka, Japan
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

Citing 3
Cited 2

Novel out-of-band signaling for seamless interworking betweem heterogeneous networks

IEEE Wireless Communications
Media-independent handover for seamless service provision in heterogeneous networks

IEEE Communications Magazine
“Optimal” Hopfield network for combinatorial optimization with linear cost function

IEEE Transactions on Neural Networks

Design and Implementation of an Optimal Radio Access Network Selection Algorithm Using Mutually Connected Neural Networks

KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Design and implementation of a user-centric access point selection algorithm based on mutually connected neural networks

Intelligent Decision Technologies - Special issue on design of intelligent environment

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a distributed radio access network selection method for heterogeneous wireless network environment, in which mobile terminals can adaptively and seamlessly handover among different wireless access technologies. Our algorithm optimizes fairness of radio resource usage without centralized computing on the network side. As a decentralized optimization scheme, we introduce the dynamics of the mutually connected neural network dynamics, whose energy function autonomously minimizes by distributed update of each neuron. Since the objective function of the fairness becomes a fourth-order function of the neurons' states which cannot be optimized by the conventional Hopfield neural network, we apply a neural network model extended to higherorder mutual connections and energy functions. By numerical simulation, we confirm that the proposed algorithm can optimize fairness of the throughput by distributed and autonomous computation.