This thesis collects the research works I performed as a Ph.D. candidate, where the common thread running through all the works is Bayesian reasoning with applications in wireless networks. The pivotal role in Bayesian reasoning is inference: reasoning about what we don’t know, given what we know. When we make inference about the nature of the world, then we learn new features about the environment within which the agent gains experience, as this is what allows us to benefit from the gathered information, thus adapting to new conditions. As we leverage the gathered information, our belief about the environment should change to reflect our improved knowledge.
This thesis focuses on the probabilistic aspects of information processing with applications to the following topics: Machine learning based network analysis using millimeter-wave narrow-band energy traces; Bayesian forecasting and anomaly detection in vehicular monitoring networks; Online power management strategies for energy harvesting mobile networks; Beam training and data transmission optimization in millimeter-wave vehicular networks. In these research works, we deal with pattern recognition aspects in real-world data via supervised/unsupervised learning methods (classification, forecasting and anomaly detection, multi-step ahead prediction via kernel methods). Finally, the mathematical framework of Markov Decision Processes (MDPs), which also serves as the basis for reinforcement learning, is introduced, where Partially Observable MDPs use the notion of belief to make decisions about the state of the world in millimeter-wave vehicular networks.
The goal of this thesis is to investigate the considerable potential of inference from insightful perspectives, detailing the mathematical framework and how Bayesian reasoning conveniently adapts to various research domains in wireless networks
