# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 4 Communication Handover  
Handover refers to the mechanism through which mobile devices transition between networks. In this research, the focus is on UAVs as moving devices. As a UAV travels along its route, it must switch from one network tower (or base station) to another as it enters and exits the coverage areas of these stations. Handovers can be a switching of networks between the same type of network (horizontal handover) or to a different type of network (vertical handover). Vertical handovers also include handovers to non-terrestrial networks like satellite networks and high-altitude platforms (HAPs) to ensure reliable and robust data links with flights and UAVs traveling above remote areas [4; 36]. This process is illustrated in Figure 3.  
There are many handover strategies discussed and studied for satellites. One of the strategies proposed is by [PERSON] et al. [37], by whom an assisted soft handoff algorithm was introduced, building on the WCDMA system's soft handoff approach and using location information to calculate the residence time. However, this method was proposed for GEO satellites. For LEO satellite-based communication systems, [PERSON] et al. proposed handovers based on a 'potential game' for mobile terminals [38]. There are other strategies for handovers that use the tracking time, signal quality, and maximum separation angle as the decision parameter [39]. While there are multiple handover strategies, entropy-based handovers are preferred for urban air mobility applications [40]. This is because of the following reasons:  
* Entropy-based methods can dynamically adjust to changes in various link parameters like the received signal strength, latency, capacity of satellite, etc., reducing the likelihood of dropped connections. * UAM vehicles, such as drones and air taxis, move at high speeds and altitudes, which require rapid adjustments in network connections. Entropy-based systems can handle these dynamics more effectively than traditional methods [41]. * This method is scalable. When more satellites or parameters are introduced, appropriate weights can be designated for them. Entropy-based handover calculates the entropy of various handover criteria--such as the signal strength, network load, and user mobility--to determine their respective variability and impact on the network. Criteria with lower entropy (less variability) are considered more stable and are given a heavier weight in the decision-making process. This weighting helps in prioritizing handover targets based on a comprehensive, data-driven evaluation. The entropy weight method is employed to evaluate the spread of values across various criteria or indices. It quantifies the level of differentiation among these criteria, allocating increased weights to indicators that show more significant dispersion [41]. The mathematical formulation of the entropy method, essential for decision-making in signal handover strategies, is detailed in Equations (14) and (18), as per [40]. The criteria for a handover are as follows:  
\[P_{new}>P_{old}+\text{Entropy} \tag{14}\]  
Here, \((P_{new})\) and \((P_{old})\) represent the power from the new and the old base stations, respectively. The term 'Entropy' in this context is derived from the entropy weight method, illustrated in Equation (15):  
\[E_{i}=-\frac{\sum_{j=1}^{n}p_{ij}\cdot\ln p_{ij}}{\ln n} \tag{15}\]  
In this equation, \(p_{ij}\) stands for the normalized values for the \(i^{th}\) indicator and \(j^{th}\) sample, defined as follows:  
\[p_{ij}=\frac{x_{ij}}{\sum_{j=1}^{n}x_{ij}} \tag{16}\]  
For the analysis, three ground base stations and one satellite were considered. Given that there are four base stations in total (\(n=4\)), the specific entropy for signal strength is given as follows:  
\[E_{i}=-\frac{\sum_{j=1}^{4}p_{ij}\cdot\ln p_{ij}}{\ln(4)} \tag{17}\]  
Figure 3: Visualization of handovers.