Machine Learning for Spectrum Sharing in Millimeter-Wave Cellular Networks

This chapter is devoted to the use of machine learning (ML) tools to address the spectrum-sharing problem in cellular networks. The emphasis is on a hybrid approach that combines the traditional model-based approach with a (ML) data-driven approach. Taking millimeter-wave cellular network as an application case, the theoretical analyses and experiments presented in the chapter show that the proposed hybrid approach is a very promising solution in dealing with the key technical aspects of spectrum sharing: the choice of beamforming, the level of information exchange for coordination and association, and the sharing architecture. The chapter then focuses on motivation and background related to spectrum sharing. It also presents the system model and problem formulation, and focuses on all technical aspects of the proposed hybrid approach. Finally, the chapter discusses further issues and conclusions.