# Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks
## 1 Introduction  
The rapid development of digital technologies in different application areas demands an expansion of infrastructure to support the communication needs of the immense number of newly introduced devices. Whether it is broadband internet services [1], mobile communication [2], the concept of smart cities or smart homes [3], maritime and aviation communication [4; 5], etc., stable, reliable and fast communication is essential. For certain applications and scenarios, terrestrial 5G networks offer more than suitable conditions, but on the other hand, based on specific needs, communication through satellite networks can be used to overcome the existing shortcomings of other available communication systems [6; 7]. There are many types of satellites that can be used for establishing communications, and they can be generally divided into four categories based on their type of orbit: Geostationary Earth Orbit (GEO), Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Highly Elliptical Orbit (HEO) satellites [8]. Because of their consistently low distancefrom the Earth's surface, LEO satellites offer the smallest amount of latency and higher throughput, which can be crucial for data transmission in many different applications. Although LEO satellites offer low latency and potentially high throughput, they present certain challenges. They have a high speed relative to the Earth's surface, and although they can cover remote areas where there is no other infrastructure, their speed can cause a Doppler shift in frequency that needs to be accounted for. In addition, since they move quickly, it is necessary to have appropriate handover procedures since the LEO satellites circle the Earth in less than two hours, so from a single point, they are only visible for a few minutes [9]. Signal quality improvement in LEO satellite systems can also be performed from many different viewpoints, whether it is through antenna design, beamforming algorithms, channel state or signal-to-noise ratio (SNR) prediction, etc. ; there are many opportunities for optimization in LEO satellite communication. When considering the practical applications of satellite communication, it is important not only to consider the SNR or the channel state information (CSI) [10] but also consider the protocol that is used for communication, and in the proposed research, the Digital Video Broadcasting Satellite Second Generation extended (DVB-S2X) protocol [11] is adopted. The initial version of the protocol was developed for GEO satellite communication and is mostly used for video broadcasting, but the DVB-S2X also supports data transmission, which is more sensitive to propagation delay, and it can be applied to LEO satellite systems. The DVB-S2X offers a range of spectral efficiencies (0.2-5.6 b/s/Hz), and its flexibility in terms of adaptive coding and modulation allows for dynamic optimization of bandwidth usage, opening up many possibilities for optimization. Considering that the optimization of communication processes can be quite complex and that ample amounts of data are available through both real communication setups and simulation implementations, machine learning (ML) presents itself as a promising approach that can improve communication performance in different scenarios [12]. ML algorithms rely on available data and are often used to perform direct input-output mappings. The process of ML parameter optimization that results in the mapping between the algorithms' inputs and outputs is called training, and this process is performed based on available data by trying to minimize the optimization (loss) function of the system. The complexity of possible mappings depends on the selection of the algorithm and available data, and a subset of ML that can provide not only simple mappings but mappings that consider additional limitations/constrictions and goals is called neural networks [13].