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COGNITIVE SATELLITE COMMUNICATIONS AND REPRESENTATION LEARNING FOR STREAMING AND COMPLEX GRAPHS Wenqi Liu June 24, 2019 This dissertation includes two separate topics. The first topic studies a promising dynamic spectrum access algorithm that improves the throughput of satellite communication (SATCOM) under the uncertainty environment. The other topic investigates real-time distributed representation learning for streaming and complex networks. 1 Cognitive Satellite Communications Dynamic spectrum access (DSA) allows a secondary user to access the spectrum holes that are not occupied by primary users. However, DSA is normally operated under uncertainty in a complex SATCOM environment, which could cause more spectrum sensing errors or even service disruption. In this case, DSA requires a decision-making process to optimally determine which channels to sense and access. To this end, I propose a solution that addresses the uncertainty in SATCOM to maximize the system throughput. Specifically, the DSA decision making process is formulated as a Partially Observable Markov Decision Process (POMDP) model. Simulation results prove the effectiveness of our proposed DSA strategy.

Cognitive satellite communications and representation learning for streaming and complex graphs.