## 7 Ground-Segment Optimization Aspects
### Applicable Optimization Methods for Ground Segment
#### 7.2.2 Long Short-Term Memory (LSTM)
#### 7.2.3 Iterative Ground Station Deployment based on Marginal Revenue Maximization (IGSD-MRM)

Unlike NSGA-II and LSTM, which are relatively general and proven methods in the literature, IGSD-MRM is a recent method proposed in [27]. It is an iterative optimization method for the GS planning problem in the context of LEO satellite networks. The optimization goal is to maximize the system throughput capacity \(C_{tot}\), which is defined by Equation (30), where \(C^{UL}\) is the user link capacity, \(C^{FL}\) is the feed link capacity, and \(D\) is the capacity of the LEO network. The subsets \(s_{m}\) and \(b_{n}\) represent \(m^{th}\) satellite and \(n^{th}\) beam, respectively. Both \(C^{UL}\) and \(C^{FL}\) are calculated from Equation (31), where \(B_{c}\) is the link bandwidth and \(M(. )\) is the mapping from SNR to spectrum efficiency under the DVB-S2X transmission scheme. \[C_{tot}=\sum_{m=1}^{M}\sum_{n=1}^{N}min(C^{UL}_{s_{m}b_{n}},C^{FL}_{s_{m}b_{n} },D_{s_{m}b_{n}}) \tag{30}\]

\[C=B_{c}M(\text{SNR}) \tag{31}\]

The following steps summarize the IGSD-MRM optimization procedure, adapted from [27]:

* Divide the area of interest into smaller grids. In [27], the authors divide the entire world into \(1^{\circ}\) latitude by \(1^{\circ}\) longitude and granularity grids. * Calculate the traffic demands of the satellite. In [27], the hotspot model is assumed for the traffic model, and the notation is such that \(F\) denotes the traffic demand for the grid and \(F_{s}(X_{i})\) denotes the traffic demand for satellite \(s\) at position \(X_{i}\). Calculate the marginal revenue for each possible GS deployment using the following:

Calculate the spherical distance between the satellite position and the GS, then calculate the corresponding geocentric angle. Calculate the path loss, link budget, and spectrum efficiency. Calculate the achievable link capacity. Take the gridded revenue as \(min(C(X_{i}),F_{s}(X_{i}))\)

Choose the location with the maximal marginal revenue as the GS deployment location. As for the overhead, the original paper [27] does not include a detailed analysis about computation complexity/convergence time for the method, so the literature is in need of more implementations that utilize IGSD-MRM before we can provide a generalized insight on this aspect. ### Method Comparisons for the Ground Segment

Finally, we compare the discussed optimization methods throughout this section in Figure 7.0 Note that we intend for the theoretical modeling entry to be a generalization for modeling and brute-force approaches rather than as an indicator of the performance of particular models. ## 8 User-Segment Optimization Aspects

The user segment can be seen as the largest segment in terms of optimization challenges, as it has the highest number of metrics and parameters related to the system optimization, and they are highly scenario- or application-specific. Even when narrowed down to the area of transportation and UAV, any optimization problem that involves a LEO satellite system is included in this user segment, and therefore, there is still a wide pool of optimization problems to be formulated and addressed. In this section, we present a few selected problems in order to focus on the user perspective, as previously presented in Table 2, but it is also worth noting that we can also optimize some aspects of the LEO space- and ground-segments from the user-segment point of view. A concrete example is the satellite-selection problem when it is expected to have a huge number of satellites in the sky. This satellite-selection problem refers to the problem of selecting the best subset of available (or visible) satellites at the receiver in order to attain specific communication, positioning, or sensing targets. This problem relies on the hypothesis that having a bigger constellation does not directly mean one can achieve a better performance metric, for example, in terms of DOP[5].