Adaptive network selection based on attractor selection in data offloading

Abstract

The unforeseen mobile data explosion poses a major challenge to the performance of today's cellular networks, and cellular network is in urgent need of original solutions to handle such voluminous mobile data. Obviously, data offloading through third-party WiFi access points (APs) can effectively alleviate the issue of overload in the cellular networks with a low operational and capital expenditure. In this paper, we study the network selection problem in operator-initiate offloading in ultradense wireless networks. To enhance the mobile data offloading, a dynamic and self-adaptive method for network selection is proposed, using the attractor selection mechanism described in biological system. In our proposed algorithm, the operator enables users to dynamically select an appropriate APs according to the dynamic conditions of various available networks. Simulation results show that the proposed algorithm decreases the service delay and achieve a high offloading efficiency in delay offloading.