Radio-strike: A reinforcement learning game for MIMO beam selection in unreal engine 3-D environments

Radio-strike: A reinforcement learning game for MIMO beam selection in unreal engine 3-D environments

Gaming and other industries are driving the development of sophisticated tools to create virtual worlds, composed of 3-D models, physics engines and other components. This talk promotes the vision that 5G and beyond will benefit from the availability of virtual worlds to leverage machine learning / artificial intelligence (ML/AI) applied to communication networks. As a concrete example of a CAVIAR (“Communication networks and Artificial intelligence immersed in VIrtual or Augmented Reality”) framework, this talk will present “Radio-Strike”, developed with Epic Games’ Unreal Engine and Microsoft’s AirSim simulator. Radio-Strike is used in the UFPA Problem Statement for the 2021 ITU AI/ML in 5G Challenge, in which participants are invited to develop a deep reinforcement learning (RL) agent that plays the role of a base station serving drones, vehicles and pedestrians via MIMO beamforming in a 3-D scenario. This problem targets those interested in designing ML/AI systems that learn from experience and outperform conventional solutions relying only on signal processing. I will provide an introduction to RL applied to MIMO beamforming, explain Radio-Strike and discuss challenges to deploy RL in 5G / 6G networks.