  In the fifth generation (5G) of mobile broadband systems, Radio Resources
Management (RRM) will reach unprecedented levels of complexity. To cope with
the ever more sophisticated RRM functionalities and with the growing variety of
scenarios, while carrying out the prompt decisions required in 5G, this
manuscript presents a lean 5G RRM architecture that capitalizes on recent
advances in the field of machine learning in combination with the large amount
of data readily available in the network from measurements and system
observations. The architecture relies on a single general-purpose learning
framework conceived for RRM directly using the data gathered in the network.
The complexity of RRM is shifted to the design of the framework, whilst the RRM
algorithms derived from this framework are executed in a computationally
efficient distributed manner at the radio access nodes. The potential of this
approach is verified in a pair of pertinent scenarios and future directions on
applications of machine learning to RRM are discussed.
