An Efficient Neural Network-Based Prediction Scheme for Heterogeneous Networks

In mobile wireless networks, the challenge of providing full mobility without affecting the quality of service (QoS) is becoming essential. These challenges can be overcome using handover prediction. The process of determining the next station which mobile user desires to transfer its data connection can be termed as handover prediction. A new proposed prediction scheme is presented in this article dependent on scanning all signal quality between the mobile user and all neighboring stations in the surrounding areas. Additionally, the proposed scheme efficiency is enhanced essentially for minimizing the redundant handover (unnecessary handovers) numbers. Both WLAN and long term evolution (LTE) networks are used in the proposed scheme which is evaluated using various scenarios with several numbers and locations of mobile users and with different numbers and locations of WLAN access point and LTE base station, all randomly. The proposed prediction scheme achieves a success rate of up to 99% in several scenarios consistent with LTE-WLAN architecture. To understand the network characteristics for enhancing efficiency and increasing the handover successful percentage especially with mobile station high speeds, a neural network model is used. Using the trained network, it can predict the next target station for heterogeneous network handover points. The proposed neural network-based scheme added a significant improvement in the accuracy ratio compared to the existing schemes using only the received signal strength (RSS) as a parameter in predicting the next station. It achieves a remarkable improvement in successful percentage ratio up to 5% compared with using only RSS.