Applying Methods of Soft Computing to Space Link Quality Prediction

The development of nano- and picosatellites for educational and scientific purposes becomes more and more popular. As these satellites are very small, high-integrated devices and are therefore not equipped with high-gain antennas, data transmission between ground and satellite is vulnerable to several ascendancies in both directions. Another handicap is the lower earth orbit wherein the satellites are usually located as it keeps the communication time frame very short. To counter these disadvantages, ground station networks have been established. One input size for optimal scheduling of timeframes for the communication between a ground station and a satellite is the predicted quality of the satellite links. This paper introduces a satellite link quality prediction approach based on machine learning.