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Efficient Space Communication and Management (SCOaM) Using Cognitive Radio Networks Based on Deep Learning Techniques: Cognitive Radio in Space Communication
Abstract
The future of space communications has evolved towards being cognitive in order to improve energy and spectrum efficiency. Nowadays, near-Earth space satellites for weather, civil defense, and public commercial sectors are rapidly increasing, thereby resulting in congestion. Cognitive digital radio is a form of dynamic wireless communication in which a transceiver can intelligently detect the parts of communication channels that are currently not in use and instantly move into vacant channels while avoiding occupied ones. A challenge for communication between satellites and ground terminals involves calibrating both the time and frequency channels during rapid relative movement. A more dynamic and highly-precise algorithm for enhancing communication between satellites and base station terminals such as deep learning in cognitive radios is proposed that enables significant degree of automation in the space communication networks where spectrum interference is a key issue.
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