Scalable Distributed Reinforcement Learning for Radio Resource Management

Abstract: There is a large potential for automation and optimization in radio access networks (RANs) using a data-driven approach to efficiently handle the increase in complexity due to the steep growth in traffic and new technologies introduced with the development of 5G. Reinforcement learning (RL) has natural applications in RAN control loops such as link adaptation, interference management and power control at different timescales commonly occurring in the RAN context. Elevating the status of data-driven solutions in RAN and building a new, scalable, distributed and data-friendly RAN architecture will be needed to competitively tackle the challenges of coming 5G networks. In this work, we propose a systematic, efficient and robust methodology for applying RL on different control problems. Firstly, the proposed methodology is evaluated using a well-known control problem. Then, it is adapted to a real-world RAN scenario. Extensive simulation results are provided to show the effectiveness and potential of the proposed approach. The methodology was successfully created but results on a RAN-simulator were not mature