Efficient Space Communication and Management (SCOaM) Using Cognitive Radio Networks Based on Deep Learning Techniques: Cognitive Radio in Space Communication

Space Communication Challenges

Demands placed on space communications systems are continuously increasing. NASA estimates that the space communications capability will need to increase nearly by a factor of 10 each for the next three decades. This trend is in step with our increasing knowledge of the cosmos -- as more detailed scientific questions arise, the ability to answer them requires ever more sophisticated instruments that generate even more data. New high-resolution hyperspectral imagers put further demands on their communications system, requiring even higher data rates.

An important challenge for deep space communications systems is to maintain their extreme reliability and versatility, in order to accommodate the long system lifetimes of most planetary missions. These challenges must be met with a communications system that requires maximum utilization of minimal available spectrum. The Space Network consists of antenna placed at three locations around the world and forms the ground segment of the communications system for space missions. These facilities are approximately 120 longitude degrees apart on Earth and provide continuous coverage and tracking for deep space missions. A large portion of deep space communications research addresses communications system engineering, radios, antennas, transmitters, signal detectors, modulation techniques, channel coding theory, data compression, and simulation.