REINFORCEMENT LEARNING FOR SCHEDULING AND MIMO BEAM SELECTION USING CAVIAR SIMULATIONS

This paper describes a framework for research on Reinforcement Learning (RL) applied to scheduling and MIMO beam selection. This framework consists of asking the RL agent to schedule a user and then choose the index of a beamforming codebook to serve it. A key aspect of this problem is that the simulation of the communication system and the artificial intelligence engine is based on a virtual world created with AirSim and the Unreal Engine. These components enable the so-called CAVIAR methodology, which leads to highly realistic 3D scenarios. This paper describes the communication and RL modeling adopted in the framework and also presents statistics concerning the implemented RL environment, such as data traffic, as well as results for three baseline systems.