Learning Radio Resource Management in 5G Networks: Framework, Opportunities and Challenges

TL;DR

This paper proposes a lean 5G Radio Resources Management architecture leveraging machine learning and network data to handle increasing complexity efficiently, with verified potential in specific scenarios.

Contribution

It introduces a unified machine learning framework for 5G RRM that shifts complexity to the framework design and enables distributed algorithm execution.

Findings

Framework effectively manages RRM complexity

Distributed algorithms run efficiently at radio access nodes

Potential demonstrated in key 5G scenarios

Abstract

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.