# Handover Decision-Making Algorithm for 5G Heterogeneous Networks
## 2 Literature Review
\begin{table}
\begin{tabular}{c c} \hline \hline
**User Preference** & **Description** \\ \hline Gold Cost & Has the best quality of service (QoS) but it does not consider the cost of the network \\ \hline Silver Cost & Balance in cost and QoS \\ \hline Bronze Cost & Cost is considered more important than the QoS parameter \\ \hline \hline \end{tabular}
\end{table} 
Table 1: User Preference algorithm [9]. algorithm was designed for telecardiology applications. During the critical condition where the patient is in emergency status, the algorithm is biased towards high-quality networks to maintain the QoS at the highest level. For non-critical situations, the algorithm is biased towards the low-cost network. The algorithm improves user satisfaction levels in terms of cost and QoS. The main drawback of this handover algorithm is that it will induce a high number of handovers if it continually searches for and connects to the small cell networks that offer better QoS. Additionally, user mobility profile, such as velocity, was not considered. Therefore, it is not suitable for performing handovers in a high-speed scenario. [PERSON] and [PERSON] [13] proposed using TOPSIS with a knapsack approach for handover between WiMAX and WLAN networks. The algorithm is focused on giving the best user satisfaction in terms of QoS. This was enabled by the knapsack approach, where the QoS Traffic parameter was calculated for the TOPSIS algorithm. The algorithm successfully reduces the handover failures and improves network performance compared with a traditional approach. However, they only tested in a low-speed environment, which is below 5 m/s. The number of handovers would increase while the MT moves at high speed. [PERSON] et al. [14] proposed an intelligent intersystem handover algorithm that integrated the grey prediction theory (GPT), TOPSIS, fuzzy analytic hierarchy process (FAHP), and principal component analysis (PCA). The GPT is used to predict future RSS values, and FAHP calculates the assigned parameter weightage based on user preference. PCA evaluates the network QoS and selects the best network. The algorithm provides better network throughput. [PERSON] and [PERSON] [15] proposed a FAHP-based MADM handover algorithm that evaluates the priority vectors to decide when the handover should be initiated. The handover criteria considered were the signal-to-noise interference ratio, speed and MT moving direction. The algorithm successfully reduces the number of unnecessary handovers. Intelligent handover decision-making schemes have varying degrees of complexity and intelligence. They have the advantage of handling complex and large parameters [16]. The intelligent-based handover decision-making algorithms have better handover performance but are more complicated and have higher handover latency than the MADM algorithm [17]. Furthermore, the unnecessary handover rate in [14; 15] will increase proportionally with the increase of MT speed unless the algorithm is biased to large cell networks during the high-speed scenario. In such a case, it sacrifices the connection to small cell networks (SG), which provides a high QoS. MADM handover scheme that is integrated with the neural network has been proposed by [PERSON] et al. [7] for heterogeneous wireless networks that involved WLAN, 4G, and 5G. The parameters involved were user data speed, Max and Min transmission rate, SINR, bit error rate (BER), and packet loss rate (PLR). The algorithm predicts the download rate of the available networks and the network with the highest download rate will be selected as a handover target. The handover success rate of this scheme is up to 90%, which shows efficient handover and seamless connectivity between the networks. However, the experiment was tested at a low moving speed only and the problem of unnecessary handover was not considered by the authors. Furthermore, this algorithm is more complicated, as it needs to conduct training and learning processes. [PERSON] et al.