Reinforcement Learning-based Optimization in 5G and Beyond Radio Access Networks

This is about teaching mobile networks to "learn by trial and error" like a self-driving car in a simulator, so that base stations can automatically figure out the best way to allocate radio resources, tune parameters, and manage traffic in 5G and future networks without engineers constantly retuning them by hand.