# Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks
## 1 Introduction
### Paper Contributions and Structure  
The work presented in this paper focuses on the application of ML (including neural networks) for SNR predictions in LEO satellite communications. The approach is based on an implemented simulator that creates channels with the following:  
* Varying expected SNR values;
* Levels of Doppler shift;
* Shadowing levels. The simulator is used to create data from 90 different channels (six different expected SNR values, five different levels of Doppler shift, and three different levels of shadowing) in order to study the relationship between different channel conditions and the improvements that can be provided by various ML algorithms for single-channel communication in comparison to a baseline method. The proposed methods are evaluated not only in terms of SNR prediction but also in terms of spectral efficiency and transmission error rate using the DVB-S2X protocol, placing a focus on practical applications. Finally, the evaluation of algorithms is also performed on two separate satellite channels, observing each pair of channels within the same level of shadowing. We propose a systematic approach, observing the relationship between different channel conditions and improvement in LEO satellites' spectral efficiency following the DVB-S2X protocol, that is, to the best of the authors' knowledge, not present in the literature. In addition to various scenarios, a proposed neural network algorithm with a modified loss function also provides an improvement when compared to the traditional, outdated information approach both when observing a single satellite and two separate satellite channels. This new approach to obtaining a reliably low transmission error rate while still consistently providing spectral efficiency improvement opens up possibilities for future work based on the modification of neural network loss functions in order to meet the required system optimization criteria. In terms of applications, the improved prediction and spectral efficiency also allow for user-centric handover procedures, and the proposed work also evaluates the possibilities for lowering outage probabilities when using two satellites instead of a single one. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Communication Method** & **Optimization Criteria** & **Optimization Method** \\ \hline \multirow{3}{*}{[22] [PERSON] et al.} & \multirow{3}{*}{high throughput GEO satellite} & Fairness among users and & fixed-size user grouping & algorithm, calculation of \\  & & spectral efficiency & equivalent CSIs and frame & \\  & & scheduling & \\ \hline \multirow{3}{*}{[23] [PERSON] et al.} & multiuser hybrid & the ergodic capacity with & \multirow{3}{*}{analytical form of the achievable ergodic capacity} \\  & satellite-terrestrial amplify & co-channel interference and & & \\  & and forward relay network & outdated CSI & & \\ \hline \multirow{3}{*}{[24] [PERSON] et al.} & MIMO LEO satellite & CSI MMSE & \multirow{3}{*}{Asymptotic MMSE and a two-stage channel estimation} \\  & & & & \\ \hline \multirow{3}{*}{[25] [PERSON] et al.} & MIMO LEO satellite system & CSI MSE & \multirow{3}{*}{A system based on neural networks} \\  & & & & \\ \hline \multirow{3}{*}{[26] [PERSON] et al.} & MIMO adaptive modulation & \multirow{3}{*}{CSI absolute error and spectral efficiency} & Multiple ML models and \\  & and coding LEO satellite & & efficiency & modified ARIMA \\  & & & \\ \hline \multirow{3}{*}{[27] [PERSON] et al.} & LEO satellite & Atmospheric attenuation in CSI & Different types of neural networks \\  & & & \\ \hline \multirow{3}{*}{[28] [PERSON] et al.} & Massive MIMO LEO satellite & \multirow{3}{*}{CSI NMSE} & LSTM \\  & system & & \\ \hline \multirow{3}{*}{The proposed paper} & LEO satellites and DVB S2X & \multirow{3}{*}{
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\end{table} 
Table 1: _Cont._The remainder of the paper is structured as follows.