# Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks
## 4 Results and Discussion
### Discussion  
When discussing the results obtained in this paper, it is not feasible to perform a direct comparison with the metrics obtained from other related work as the experimental setup and goals are so diverse. On the other hand, when comparing the methods and approaches that are present, it is possible to see how the performed research is compatible with other approaches and what could be some of the limitations of the approach considered in this paper.  
The results obtained in this paper show that neural networks can successfully be used to predict the SNR values for channels with various characteristics. This stands in line with the results obtained from existing work where different neural network architectures were also shown to be successful in predicting CSI in LEO satellite systems [25; 26; 28]. The presented paper also provides a thorough evaluation of a multitude of different channels by using the implemented simulator and provides a novel approach in terms of improving  
Figure 13: The achieved spectral efficiencies on the test set for various scenarios under heavy shadowing conditions using two channels, comparing OI and NN2. The color of each square represents a relative improvement of the achieved spectral efficiency calculated as \(\left(M_{i}^{NN2}-M_{i}^{OI}\right)/M_{i}^{OI}\).  
spectral efficiency while keeping the transmission error rate above a fixed threshold. The observed thresholds for the transmission error rate (0.01 and 0.001) are not suitable for all applications, but the paper presents a foundation that can be used for future improvements and provides a proof of concept that can be combined with other optimization strategies, such as weather influence [27] and energy efficiency [20].  
When discussing the barriers to the practical implementation of the proposed method in terms of energy consumption and computation time, it is important to note that the approach uses a relatively small convolutional neural network that contains 6261 parameters in combination with a simple subtraction of the estimated margin. The time needed to infer a prediction on a single batch containing 64 inputs is 18.7 ms on an AMD Ryzen 7 7840 HS CPU. Depending on the usage, it is understood that the current complexity might present an obstacle, but smaller architectures of neural networks could be tested, and the length of the prediction could also be changed to not be a single point but rather several points so the need for inference is not as frequent.  
The presented approach has several limitations that should be mentioned. The presented work analyzes three different factors (shadowing, \(f_{Dm}\), and expected SNR) but does not take into account other factors that could influence the channel, so this could be considered in future research. Furthermore, the paper observes a maximum of two satellites, and the scaling of the proposed method into larger systems, both in terms of implementation complexity and efficiency, is yet to be analyzed. The proposed solution is also focused on the DVB-S2X protocol, and its applications in different communication scenarios or with different satellite types were not in the scope of the paper. Finally, there is also an in-depth analysis of different neural network architectures, which was omitted in this paper as it is not the focus, but obtaining better results with smaller models always presents a broad area for future research.