Coexistence Management for URLLC in Campus Networks via Deep Reinforcement Learning

Abstract

Increased usage of wireless technologies in unlicensed frequency bands inevitably increases the co-channel interference. Hence, for applications such as ultra-reliable-low-latency-communications (URLLC) in factory automation, the interference should be avoided. An intelligent coexistence management entity, which dynamically distributes the time and frequency resources, has been shown to be greatly beneficial in boosting efficiency and avoiding crippling interruptions of the wireless medium. This entity also supports multi-connectivity schemes, which are crucial for industry-level reliability requirements. The proposed governing technique of the coexistence management is a deep reinforcement learning (DRL) method, which is a model-free framework and channel allocation decisions are learned merely by interactions with the environment. The simulation results have shown that the employed method can greatly increase the reliability of the wireless network, when compared with legacy methods.