Survey on Optimization Methods for LEO-Satellite-Based Networks with Applications in Future Autonomous Transportation.

Future autonomous transportation is one of the most demanding application areas in terms of connectivity, as it has to simultaneously meet stringent criteria that do not typically go hand in hand, such as high throughput, low latency, high coverage/availability, high positioning and sensing accuracies, high security and robustness to interferences, etc. In order to meet the future demands of challenging applications, such as applications relying on autonomous vehicles, terrestrial networks are no longer sufficient and are to be augmented in the future with satellite-based networks. Among the emerging satellite networks, Low Earth Orbit (LEO) networks are able to provide advantages over traditional Medium Earth Orbit (MEO) and Geo-Stationary Earth Orbit (GEO) networks in terms of signal latency, cost, and performance.

Collaborative Solutions for Interference Management in GNSS-Based Aircraft Navigation.

Nowadays, the Global Navigation Satellite Systems (GNSS) technology is not the primary means of navigation for civil aviation and Air Traffic Control, but its role is increasing. Consequently, the vulnerabilities of GNSSs to Radio Frequency Interference, including the dangerous intentional sources of interference (i.e.

Jammer Classification in GNSS Bands Via Machine Learning Algorithms.

This paper proposes to treat the jammer classification problem in the Global Navigation Satellite System bands as a black-and-white image classification problem, based on a time-frequency analysis and image mapping of a jammed signal. The paper also proposes to apply machine learning approaches in order to sort the received signal into six classes, namely five classes when the jammer is present with different jammer types and one class where the jammer is absent. The algorithms based on support vector machines show up to 94 .