# Handover Decision-Making Algorithm for 5G Heterogeneous Networks
## 4 Experimental Setup  
The proposed handover is simulated by 100 loops in the heterogeneous wireless network environment that consists of 4G LTE, 5G, and WLAN networks, as shown in Figure 4. The 4G LTE has the most comprehensive network coverage, covering all the 5G and WLAN networks. This assumes that the MT initially connects to the 4G LTE and moves from point A to destination B traverses the heterogeneous networks at high speed (40-100 km/h). The simulation parameters are tabulated in Table 2[7; 12]. The user usually prefers the WLAN due to its high bandwidth and low cost. However, the network coverage of WLAN is very limited. Therefore, many studies have excluded WLAN from the high-speed scenario and have prioritized a 4G network that has large network coverage [20]. In the new era, the 5G network will be a future trend for wireless communication because it provides the best network quality among wireless network candidates. However, the 5G network coverage is up to 300 m only [21]. In this work, the proposed handover algorithm monitors the network quality at the time interval of \(z\). The time interval is adjusted automatically according to the MT velocity, given as  
\[z=\frac{D}{v} \tag{16}\]  
Figure 4: Experimental setup for the heterogenous wireless network’s environment. where \(D\) is 1 m and \(v\) is the MT velocity that can be obtained through the vehicle speedometer. We are assuming the MT only collects data and passes them to the network orchestration for handover processing. Therefore, the proposed algorithm will not overload the MT. The proposed handover algorithm takes the mean value of RSS samples to minimize the oscillation of RSS signals. The RSS values, \(RSS_{m}\) can be obtained using Equation (17) [22]. The formula for calculating the RSS mean value, \(\overline{RSS}\), is given in Equation (18). \[RSS_{m}=P_{TX}-PL_{0}-10 nlog\frac{R}{d_{0}}+\varepsilon \tag{17}\]  
\[\overline{RSS}=\frac{\sum_{i=1}^{o}RSS_{m}}{q} \tag{18}\]  
where \(q\) is the number of RSS values taken, \(P_{TX}\) is the transmit power, \(PL_{0}\) is the power loss at the reference point, \(n\) is the path loss exponent, \(d_{0}\) is the distance between the AP and a reference point, and \(\varepsilon\) is a zero-mean Gaussian random variable caused by shadow fading. The difference between RSS signals found by taking a single RSS sample and the mean value of multiple RSS samples is shown in Figure 5. The RSS signal using the mean value is smoother than the RSS signals shown in Figure 6. This could minimize the ping-pong effect in heterogeneous networks. The distance threshold, \(l_{th}\), is the product of the MT velocity (\(v\)) and dwelling time threshold. The two seconds dwelling time threshold for different MT velocities yields different travelling distance thresholds, as indicated in Figure 7. The \(l_{th}\) is increased proportionally with the MT velocity. Table 3 displays the trajectory distance within each network based on the network layout in Figure 4. The predicted distance is the distance calculated by the dwelling time algorithm. The Table 3 results for WLAN#1 and 5G#3 have more than 10% error due to the short actual travel distance when less than 20 m. However, the two networks' actual travel distances and prediction travel distances have approximately values.