Design and implementation of an optimal radio access network selection algorithm using mutually connected neural networks

Abstract

We propose a distributed and autonomous algorithm for radio resource usage optimization in heterogeneous wireless network environment. We introduce optimization dynamics of the mutually connected neural network to optimize average throughput per the terminals and the load balancing among the radio access networks (RANs). The proposed method does not require a server to collect whole information of the network and compute the optimal state of RAN selections for each terminal. We construct a mutually connected neural network by calculating the connection weights and the thresholds of the neural network to autonomously minimize the objective function. By numerical simulations, we show that the proposed algorithm improves both the total and the fairness of the throughput per terminal. Moreover, we implement the proposed algorithm on an experimental wireless network distributively, and verify that the terminals optimize RAN selection autonomously. © 2009 Springer Berlin Heidelberg.