Adaptive WiFi-offloading algorithm based on attractor selection in heterogeneous wireless networks

The unforeseen mobile data explosion poses a major challenge to the performance of today＇s cellular networks, and cellular networks are in urgent need of novel solutions to handle such voluminous mobile data. Obviously, data offioading through third-party WiFi access points （APs） can effectively alleviate the data load in the cellular networks with a low operational and capital expenditure. In this paper, we propose and analyze an adaptive WiFi-offioading algorithm based on attractor selection in heterogeneous wireless networks. In the proposed WiFi-offloading algorithm, the WiFi system throughput and cellular throughput in the coverage area of WiFi, are mapped into the activity of the attractor, which is the reflector of the current network environment. When the current attractor activity is low, the network is dominated by the noise. Then, the noise triggers the controller to select adaptive attractor, an optimal WiFi-offloading ratio 4, to adapt to the dynamic network environment. And users offload the specific portion of traffic to the WiFi networks with the ratio of φ, which improves the throughput of heterogeneous wireless networks and alleviates the load of cellular networks. Through simulation, we show that the proposed WiFi-offioading algorithm outperforms the existing ones with 21% higher heterogeneous network throughput in a dense traffic environment.