## 7 Ground-Segment Optimization Aspects
### Applicable Optimization Methods for Ground Segment
#### 7.2.2 Long Short-Term Memory (LSTM)
If the constellation is excessively large, the information provided by two or more satellites that are close to each other is highly correlated and thus not adding value when increasing the number of processed satellite signals, and we can consider that the satellite system would be overpopulated \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Convergence** & **Global** & **Large-Scale** \\  & **Spred** & **Convergence** & **Applicability** \\ \hline Theoretical & & & \\ Modeling & & & \\ \hline LSTM & & & \\ NSGA-II & & & \\ \hline IGSD-MRM & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparative Summary of Optimization Methods for LEO Ground-Segment Optimization. in such cases. In addition, the receiver processing complexity increases with the number of satellite signals to be processed; therefore, selecting the best satellites to process is important. In this case, the optimization problem becomes figuring out which of those satellites we have in view are most representative and, for example, which configuration would provide the best geometric distribution among other factors. We can optimize and select those satellites with the best score metrics (individually analyzed or combined) using the methods and metrics from Table 2.

Figure 8 shows an illustration of this optimization from the user perspective in a GNSS scenario. In Figure 8, the satellites in view depicted with a red circle are redundant (e.g., DOP contribution is bad, elevation is very low, etc.). Thus, the best approach would be that these satellites are not to be further considered, but only a subset of the available satellites (e.g.,the one with some high scores according to the defined performance metrics) is to be used for further processing. In the rest of this section, we present various metrics and parameters that are used and/or adjusted in order to keep a good general performance of the system, and we present an overview of various optimization methods that have seen used in applications in recent years. This section addresses the optimization problems from the user-segment perspective. ### Optimization Metrics Related to the User Segment

Of the metrics seen from Table 2 related to the user segment, some of them have already been presented in previous sections; for example, we presented GDOP in Equation (18), which is also utilized in the user segment for the satellite-selection problem. Thus, we will present an overview of the metrics that have not yet been covered in the other segments here. We will also omit basic parameter metrics as the definition of the parameter is sufficient enough of an explanation for these metrics, such as the antenna elevation in satellite selection, travel time and distance in UAV data acquisition or latency, and the number of ping pongs in the handover planning. It is best if we start with introducing metrics similar to GDOP that are not dependent on the models utilized in the system. One such metric is the TDOP, which is closely related

Figure 8: Example of an Optimization Problem from the User-Segment Perspective: The satellite-selection problem, i.e., the total number of satellites in view is to be reduced to an optimal subset according to application-specific optimization metrics. to GDOP as another DOP metric. It is presented in Equation (32), where the notation from Equation (18) carries, and 4 refers to the fourth element of the trace vector, which represents time, thus the name. Other DOP metrics also exist in the literature, all of which are covered in earlier studies in detail, such as in [157]. \[\texttt{TDOP}=\sqrt{tr(H^{\texttt{T}}H,\texttt{4})^{-1}} \tag{32}\]

Another metric that is seen commonly in network performance evaluation is the Bit Error Rate (BER), which is a unitless metric that is defined as in Equation (33). \[\texttt{BER}=\frac{\text{number of bit errors}}{\text{total number of transferred bits}} \tag{33}\]

BER is a very simple error metric that simply looks at the error percentage in bits.