Inference and Learning for Wireless Communications

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The objective of this research program is to contribute to the understanding of the fundamental principles, limitations, and trade-offs in the design of the Bayesian-theory based algorithms for inference and learning with emphasis on their role in transmission of information in communication networks. ******The Bayesian formalism consists in modelling of all interacting entities as random variables. It was successfully applied in many areas of science and engineering, and the recent decades witnessed a progress in Bayesian reasoning. In particular, the graph-based models which describe the relationship between the variables using graphs obtained significant notoriety leading to a well structured approach to the development of the Bayesian algorithms using the principle of message passing. Yet another step bringing the Bayesian methods closer to practice are the variational Bayesian techniques which explicitly use factorization of the messages into predefined functions to simplify the marginalization required during the message passing. ******While the Bayesian framework is well known, the approximate approaches must be used in practice and these must be devised and analyzed knowing the application context; here, the transmission of information in wireless networks. We will also leverage the progress made in the general area of machine learning or pattern recognition. However, to remove the requirement for large amount of data, which is impractical for highly variable environment characteristic of wireless communications, we will use to the generative-model approach. ******The program will focus on two main problems: 1) the analysis and correction of approximate inference algorithms, and 2) the learning from the binary observations. As a particular scenarios to study the problems of inference, we will use the context of massive MIMO receiver. The questions of learning will be study in the context of a) identification of the source of errors in point-to-point transmission; and b) modelling of interference in multiuser wireless networks. We will simultaneously consider the issue of model selection strategies, related to problems 2) which rely heavily on the prior models.******This research program will contribute the knowledge creation, as well as, will build technical expertise and contribute to the training of students in the area of Bayesian inference and learning with application to wireless communication. As witnesses by the growing presence of Bayesian techniques in many areas of applied science, as well as persistent growth of wireless networks, such an expertise is increasingly important and will benefit the students/interns involved in the project.