# Handover Decision-Making Algorithm for 5G Heterogeneous Networks
## 1 Introduction  
The rapid development of wireless technologies has led to compatibility problems between the technologies that affect handover from one to another. The trend towards attaining pervasive access over heterogeneous technologies nowadays entail the addition of a variety of existing and upcoming networking technologies for a seamless wireless communication environment. Advances in wireless communication have led to multipurpose, convenient, and inexpensive network services. The interconnection between heterogeneous networks is a critical element that enables pervasive access. Achieving a seamless handover is a challenging issue. The evolution of next-generation networks is expected to consider handover management as its main aspect [1].  
Handover management in a wireless network is a process that allows a mobile terminal (MT) to continue ongoing sessions when moving from one network access point to another. When the connection moves from one access point to another of similar technology, it is called \"horizontal handover.\" On the other hand, a vertical handoff is a handover process between two distinct networks, such as handovers between a wireless local area network (WLAN) and a fifth-generation (5G) mobile network. Handovers can also be classified as hard and soft handovers. Hard handover is the type of handover that releases the serving station signal before the new resources can be committed. This type of handover causes interruption to mobile communication during the handover process [2]. Soft handover isthe type of handover where the mobile terminal establishes a mobile connection with a new access point before the old link is released. It performs a handover without interruption.  
The handover process consists of three stages, as shown in Figure 1. The handover initiation stage detects networks and the main attributes of the network via the Media Independent Handover Function (MIHF) [3; 4]. The handover decision making chooses the best network based on network information and initiates the handover at the appropriate time. Handover execution establishes the connection with the selected network and releases the old network. Several handover criteria can be used to perform a handover decision, such as received signal strength (RSS), data rate, monetary cost, bit error rate (BER), and signal-to-interference plus noise ratio (SINR). Using multiple criteria in performing handover decisions can provide a better handover performance than conventional handover algorithms that depend on one or two handover parameters.  
Fifth-generation network is the latest mobile wireless technology and is highly preferred because of its high data transmission rate and low latency [5]. However, the 5G network cell coverage is much smaller than its predecessor, the 4G network. The MT moving at high speed will suffer frequent handovers and lead to a high number of unnecessary handovers. This may cause data corruption, delay in transmitting data, and poor user experience in the network [6]. Therefore, an accurate handover decision-making algorithm is needed for achieving seamless handover.  
The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a reliable multi-attribute decision-making (MADM) technique and has been widely applied for handover decision making in heterogeneous networks. However, most MADM handover algorithms are biased towards large cell networks, such as 4G networks, in high-moving speed scenarios. The intelligent-based handover algorithm is more complex as it requires a data training process for algorithms to learn [7]. This paper presents a handover algorithm that integrates a dwell time prediction technique with the TOPSIS method to reduce the number of unnecessary handovers in 5G heterogeneous networks. The proposed algorithm avoids unnecessary handover by estimating the connection time to a 5G small cell network using the dwell time prediction technique, while the network evaluation and selection are performed by the TOPSIS algorithm [6].