User-centric optimum radio access selection in heterogeneous wireless networks based on neural network dynamics

We propose an autonomous and decentralized optimization method for heterogeneous wireless networks, 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. For optimum selection of radio resources, we introduce dynamics of the mutually connected neural network which converges to an optimal state since its property is to autonomously decrease its energy function. In this paper, we apply such neurodynamics to user-centric optimum radio access selection, which maximizes the total throughput of the entire networks, and minimizes the power consumption of the user terminal and the communication cost. We compose a neural network that solves such a problem, and we show that they can be optimized by distributed decisions based on autonomous and simple neuron updates on the terminal side.