# Handover Decision-Making Algorithm for 5G Heterogeneous Networks
## 2 Literature Review  
A MADM algorithm uses various handover parameters, such as monetary costs, network security, available bandwidth, handover latency, and power consumption, for handover decision making [8; 9]. The most commonly used MADM algorithms are TOPSIS and simple additive weighting (SAW). Each of them works in a different way, but they all select networks by comparing different options based on multiple attributes and choosing  
Figure 1: Handover process. the network that best satisfies the conditions. According to [PERSON] et al. [10], the results of each are almost the same. An adaptive vertical handover algorithm proposed by [PERSON] [8] improves user satisfaction and power consumption using a moving average method to predict future data and reduce the measurement rate in the network discovery phase. This method requires the latest data sets for data prediction. The network discovery is only triggered to collect the network's parameters, RSS and signal delay, and the user's parameters, including network cost, security, and power consumption, when the value of the cost function uptends. Then the cost function for each discovered network is calculated. The network with the lowest cost will be selected as the handover target. The cost function is given by Equation (1). \[Cost=w_{C}\log(C)+w_{S}\log(1/S)+w_{P}\log(P)+w_{RSS}\log(1/RSS)+w_{D}\log(D) \tag{1}\]  
where \(Cost\) is the cost function of the network; \(C\) is the network cost for the user; \(S\) refers to the security level of the networks (higher level better network security); \(P\) represents power consumption; \(RSS\) is the received signal strength of the network; \(D\) is the delay of the network; and \(w_{C}\), \(w_{S}\), \(w_{p}\), \(w_{RSS}\), and \(w_{D}\) are the respective weights of the above parameters, and their total weightage is 1, as shown in Equation (2). \[w_{C}+w_{S}+w_{P}+w_{RSS}+w_{D}=1 \tag{2}\]  
The prediction of the cost function considers the user's preference. If network cost was set at the highest priority, the network with lower cost would be selected as an optimal network. This algorithm successfully reduces power consumption and cost. However, the algorithm might have a high number of unnecessary handovers when the MT traverses the small cell networks at high speed because the cost function did not take MT velocity into consideration. TOPSIS-based MADM handover algorithms have been presented in [9; 11; 12; 13; 14]. [PERSON] and [PERSON] [9] proposed an enhanced TOPSIS-based vertical handover algorithm for a heterogeneous network that consists of microwave access (WiMAX), WLAN, and LTE. The algorithm focuses on three user preferences: Gold Cost, Silver Cost, and Bronze Cost, as described in Table 1.  
The authors manually set the parameters' weightage for different user preferences. The handover parameters used were RSS and network usage. The algorithm reduces the number of handovers and the overall cost by up to 60% and 40%, respectively, as compared with the traditional RSS-based handover algorithm. [PERSON] et al. [11] proposed a TOPSIS-based handover algorithm and tested it using a real environment 4G network and WLAN data. The input parameter for the TOPSIS algorithm included network coverage, packet loss, jitter, latency, bandwidth, and RSS. The testing results showed that the algorithm mostly selected the 4G network because the 4G network has better network quality than WLAN. This work was only tested in a low-speed environment by entering a cafe for the WLAN connection. Furthermore, the quality of WLAN heavily depends on the internet plan subscribed to by the cafe. [PERSON] et al. [12] proposed an improved TOPSIS-based handover algorithm in heterogeneous networks. The TOPSIS approach was used because of its multi-attribute decision analysis method, which simultaneously estimates the best and worst alternatives. The