Vertical handover algorithm in OpenAirInterface and neural network for 4G and 5G base stations

Abstract

The fifth generation (5G) mobile network has been designed to offer individuals and objects with nearly ubiquitous, ultra-high bandwidth, and low latency access. In heterogeneous wireless networks, the demand for wireless communication devices has increased dramatically. This paper provides a Vertical Handover Algorithm (VHA) in OpenAirInterface (OAI) and Neural Network (NN) for heterogeneous 4G and 5G Radio Access Networks (RANs). The framework of the algorithm is built by configuring the network environment in which we employ network resources when switching between OAI 4G and OAI 5G base stations. Each base station establishes its three-layer backpropagation neural network model. Then, the user’s speed, signal-to-interference, and noise ratio (SINR), maximum transmission rate, minimum delay, coverage area, bit error rate, and packet loss rate are used as reference objects to participate in the setting of VHA using a backpropagation neural network model. The measured download/upload speeds are used to compare the performance of the two wireless networks, and the vertical handover algorithm then chooses the best wireless network for performing the vertical handover decision.