In industrial environments, an increasing amount of wireless devices are
used, which utilize license-free bands. As a consequence of these mutual
interferences of wireless systems might decrease the state of coexistence.
Therefore, a central coexistence management system is needed, which allocates
conflict-free resources to wireless systems. To ensure a conflict-free resource
utilization, it is useful to predict the prospective medium utilization before
resources are allocated. This paper presents a self-learning concept, which is
based on reinforcement learning. A simulative evaluation of reinforcement
learning agents based on neural networks, called deep Q-networks and double
deep Q-networks, was realized for exemplary and practically relevant
coexistence scenarios. The evaluation of the double deep Q-network showed that
a prediction accuracy of at least 98 % can be reached in all investigated
scenarios.Comment: Submitted to the 23rd IEEE International Conference on Emerging
Technologies and Factory Automation (ETFA 2018