Vertical handover algorithm based on multi-attribute and neural network in heterogeneous integrated network

Xiaonan Tan,Geng Chen,Hongyu Sun

doi:10.1186/s13638-020-01822-1

Abstract

A novel vertical handover algorithm based on multi-attribute and neural network for heterogeneous integrated network is proposed in this paper. The whole frame of the algorithm is constructed by setting the network environment in which we use the network resources by switching between UMTS, GPRS, WLAN, 4G, and 5G. Each network build their own three-layer BP (Back Propagation, BP) neural network model and then the maximum transmission rate, minimum delay, SINR (signal to interference and noise ratio, SINR), bit error rate, user moving speed, and packet loss rate which can affect the overall performance of the wireless network are employed as reference objects to participate in the setting of BP neural network input layer neurons and the training and learning process of subsequent neural network data. Finally, the network download rate is adopted as prediction target to evaluate performance on the five wireless networks and then the vertical handover algorithm will select the right wireless network to perform vertical handover decision. The simulation results on MATLAB platform show that the vertical handover algorithm designed in this paper has a handover success rate up to 90% and realizes efficient handover and seamless connectivity between multi-heterogeneous networks.

Highlights

In recent years, with the rapid development of wireless network technology and the rapid advancement of network access technology of the heterogeneous convergence architecture, many different types of wireless networks exist simultaneously [1, 2]
We introduce the Back propagation (BP) neural network to participate in the construction and execution of this algorithm, and introduce the The 5th generation mobile communication technology (5G) network in the environment where Universal mobile telecommunications system (UMTS), General packet radio service (GPRS), Wireless local area network (WLAN), and The 4th generation mobile communication technology (4G) networks coexist to improve the scope of this algorithm
5 Conclusions The vertical handover algorithm designed in this paper introduces the BP neural network model theory and integrates the theory into its running process, completing the three-layer BP neural network based on UMTS, GPRS, WLAN, 4G, and 5G

Summary

Introduction

With the rapid development of wireless network technology and the rapid advancement of network access technology of the heterogeneous convergence architecture, many different types of wireless networks exist simultaneously [1, 2]. Reference [3] proposed a vertical handover decision algorithm for cooperation of multi-terminal based on fuzzy logic and analytic hierarchy process It could fully reflect the application requirements and user preferences, it lacked consideration of the upcoming 5G network. The vertical handover algorithm proposed in reference [8] mainly used “New Composite Rule Inference based logarithm” to improve the service quality between WLAN_UMTS (Wireless Local Area Network and Universal Mobile Telephone System) networks It had a high handover success rate, it did not integrate the 4G and 5G network. Reference [12] presented a mixed integer linear programming (MILP) model to balance multi-homing loads in heterogeneous wireless networks based on multi-objective tabu search method Both the reference [13] and the reference [15] applied the genetic algorithm to the relevant handover decision, which had a high success rate of handover, and lacked consideration of 5G network factors.

Proposed method

Findings

Conclusions