Resource Allocation for a Wireless Coexistence Management System Based on Reinforcement Learning
This addresses interference management for wireless systems in industrial settings, but it is incremental as it applies existing RL methods to a specific domain.
The paper tackles the problem of mutual interference among wireless devices in industrial environments by proposing a reinforcement learning-based resource allocation system, achieving at least 98% prediction accuracy for conflict-free resource utilization in simulations.
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.