Machine-Learning Based Sensing and Waveform Adaptation for SDRs Operating in Congested and Contested Environment

Unprecedented growth in demand of wireless devices has caused overcrowding of the spectrum. Modern Software Defined Radios (SDRs) have to provide satisfactory services while transmitting/receiving in congested and contested environment. In order to do that, complex learning algorithms have to be paired with capable, flexible and wideband hardware.Space Micro and its partner research institution, the University of Arizona, bring together innovations in SDR design and implementation and Machine Learning (ML). Together, these innovations will provide the Army with improved radio systems that learn from data, identify patterns and optimize transmissions in a congested/jammed environment with minimal human intervention. Space Micro is a fast growth, high technology space and terrestrial SDR business founded in 2002. Dr. Marwan Krunz and his team are nationally recognized for their unique expertise in artificial intelligence, machine learning and cognitive waveforms-all of which are crucial to this project. Space Micro and the University of Arizona have collaborated effectively on previous projects. During this STTR, Space Micro and University of Arizona will work together to provide the Army with a fully cognitive ML driven SDR platform.