## 7 Ground-Segment Optimization Aspects
### Applicable Optimization Methods for Ground Segment
#### 7.2.2 Long Short-Term Memory (LSTM)
The algorithm uses different metrics, besides the DOP, in order to weight the final optimal subset of satellites. In [163], the authors propose a brute-force method, in which they compute DOP metrics for the different subsets of all satellites in view and take as the optimal subset those satellites with the lowest DOP metric. Finally, in [164], the authors analyze the optimal number of satellites to be selected until the DOP metric becomes saturated. Thus, including a higher number of satellites does not improve the metrics significantly. ### Illustrative Simulation-Based Examples

In order to show examples of some of the metrics described in the previous subsection, we have carried out some simulations with specific LEO constellations. Oneweb and Starlink LEO constellations were selected as more representative and promising among the future LEO mega-constellations. Figure 9 shows the coverage map for these two LEO constellations considering two different elevation masks, \(10^{\circ}\) and \(50^{\circ}\), respectively. Moreover, the average number of satellites in view in the whole Earth is also shown. We notice from Figure 9a,c that, in both OneWeb and Starlink constellations, when a small elevation angle is considered (e.g., \(10^{\circ}\)), the average number of satellites in view is very large, with more than 100 and more than 300 satellites in view for Oneweb and Starlink, respectively. These values were obtained considering the designed planned constellations, which will be operative in the coming years. The planned constellations are designed for having 7808 and 34,404 for Oneweb and Starlink, respectively, distributed at different altitudes and orbital planes configurations, with a Walker star constellation topology. The visibility conditions were configured as those satellites with a higher elevation angle than the chosen elevation masks (i.e., \(10^{\circ}\) or \(50^{\circ}\)) from the user-location perspective. In addition, \(10^{4}\) Earth user locations were considered for all the simulations in Figures 9-11. To better model the orbits, a Simplified Deep Space Perturbation model (SDP4) [165] was used along with the Matlab Satellite Communication Toolbox. The SDP4 model accounts for the effects of the oblateness of the Earth and atmospheric drag effects in the satellite orbit, among other effects. Figure 10 depicts DOP metrics, namely GDOP, Position Dilution of Precision (PDOP), TDOP, Vertical Dilution of Precision (VDOP), and Horizontal Dilution of Precision (HDOP), for Oneweb and Starlink constellations. From Figure 10, we can observe that both constellations offer a similar DOP performance. GDOP is \(<\)1 for both Oneweb and Starlink, respectively, but only when considering a low elevation mask (e.g., \(10^{\circ}\)). If the minimum elevation to consider visibility is increased, the amount of satellites contributing to the measurements is largely decreased, as well as its spatial diversity, negatively influencing to these DOP values. For example, GDOP measurements considering a high elevation mask (e.g., \(50^{\circ}\)) become approximately 12 in both constellations. The rest of DOP metrics are kept below 1 in both cases with a low elevation mask and much larger considering a high

elevation mask. DOP metrics are examples of optimization metrics that can be inserted in user-segment optimization. Finally, Figure 11 shows the \(C/N_{0}\) probability distribution function for Oneweb and Starlink LEO constellations, respectively. For the simulations, we considered a rural scenario with a Line of Sight (LOS) condition. This means that direct vision between user receiver and satellites was achieved, as well as low density of scatters and multipath components. To simulate a realistic satellite-to-ground channel in the described scenario, we used the freely available QuaDRiGa [166; 167] framework. This channel model is a Matlab-based software developed by Fraunhofer HHI that enables the modeling of radio wireless channels by generating realistic radio channel impulse responses.