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

Abstract

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. v 2 Distributed Real-time Representation Learning of Large Networks Large-scale networks have attracted significant amount of attentions to extract and analyze the hidden information from big data. In particular, graph embedding learns the representations of the original network in a lower vector space while maximally preserving the original structural information and the similarity among nodes. I propose a real-time distributed graph embedding algorithm (RTDGE) which is capable of distributively embedding the streaming graph data by combining a novel edge partition approach and an incremental negative sample approach. Furthermore, a real-time distributed streaming data processing platform is prototyped based on Kafka and Storm. On this platform, real-time Twitter network data can be retrieved, partitioned and processed for state-of-art tasks including synonymic user detection, community classification and visualization. For complex knowledge graphs, existing works cannot capture the complex connection patterns and never consider the impacts from complicated relations, due to the unquantifiable relationships. In this dissertation, a novel hierarchical embedding algorithm is proposed to hierarchically measure the structural similarities and the impacts from relations by constructing a multi-layer graph. Then an advanced representation learning model is designed based on an entity's context, which is generated by taking random walks on the multi-layer content graph. Experimental results show that our proposed model outperforms the state-of-the-art techniques. vi