Yonina Eldar, Vince Poor and Nir Shlezinger
Weizman Institute of Science, Princeton U, Weizman Inst. Sci.
Mobile communications and machine learning are two of the most exciting and rapidly developing technological fields of our time. In the past few years, these two fields have begun to merge in two fundamental ways. First, while mobile communications has developed largely as a model-driven field, the complexities of many emerging communication scenarios raise the need to introduce data-driven methods into the design and analysis of mobile networks. Second, many machine learning problems are by their nature distributed due to either physical limitations or privacy concerns. This distributed nature can be exploited by using mobile networks as part of the learning mechanisms, i.e., as platforms for machine learning.
In this tutorial we will illuminate these two perspectives, presenting a representative set of relevant problems which have been addressed in the recent literature, and discussing the multitude of exciting research directions which arise from the combination of machine learning and wireless communications. We will begin with the application of machine learning methods for optimizing wireless networks: Here, we will first survey some of the challenges in communication networks which can be treated using machine learning tools. Then, we will focus on one of the fundamental problems in digital communications – receiver design. We will review different designs of data driven receivers, and discuss how they can be related to conventional and emerging approaches for combining machine learning and model-based algorithms. We will conclude this part of the tutorial with a set of communication-related problems which can be tackled in a data-driven manner.
The second part of the tutorial will be dedicated to wireless networks as a platform for machine learning: We will discuss communication issues arising in distributed learning problems such as federated learning and collaborative learning. We will explain how established communications and coding methods can contribute to the development of these emerging distributed learning technologies, illustrating these ideas through examples from recent research in the field. We will conclude with a set of open machine learning related problems, which we believe can be tackled using established communications and signal processing techniques.