Inference and Learning for Wireless Communications

该研究计划的目标是有助于理解基于贝叶斯理论的推理和学习算法设计中的基本原则，限制和权衡，重点是它们在通信网络中的信息传输中的作用。 贝叶斯形式主义包括将所有相互作用的实体建模为随机变量。它在科学和工程的许多领域得到了成功的应用，近几十年来，贝叶斯推理取得了很大的进展。特别是，基于图形的模型，它描述了变量之间的关系，使用图形获得显着的恶名，导致一个良好的结构化的方法来发展的贝叶斯算法使用的消息传递的原则。使贝叶斯方法更接近实践的另一个步骤是变分贝叶斯技术，其明确地使用将消息分解为预定义函数以简化消息传递期间所需的边缘化。 虽然贝叶斯框架是众所周知的，但在实践中必须使用近似方法，并且必须在了解应用环境的情况下设计和分析这些方法;在这里，无线网络中的信息传输。我们还将利用在机器学习或模式识别的一般领域取得的进展。然而，为了消除对大量数据的需求，这是不切实际的高度可变的环境特性的无线通信，我们将使用生成模型的方法。 该程序将集中在两个主要问题：1）近似推理算法的分析和校正，以及2）从二进制观测值中学习。作为一个特定的场景来研究推理问题，我们将使用大规模MIMO接收机的上下文。学习的问题将在以下背景下进行研究：a）识别点对点传输中的错误源;和B）多用户无线网络中的干扰建模。我们将同时考虑模型选择策略的问题，与严重依赖先验模型的问题2）有关。 这项研究计划将有助于知识创造，以及，将建立技术专长，并有助于在贝叶斯推理和学习与应用到无线通信领域的学生的培训。随着贝叶斯技术在许多应用科学领域的日益普及，以及无线网络的持续增长，这种专业知识越来越重要，并将使参与该项目的学生/实习生受益。

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.