Resource Allocation for a Wireless Coexistence Management System Based on Reinforcement Learning
IEEE International Conference on Emerging Technologies and Factory Automation(2018)
摘要
In industrial environments an increasing amount of wireless devices are used, which utilize licence-free bands. As a consequence of this mutual interferences of wireless systems might decrease the state of coexistence. Therefore, a central co-existence 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 utilisation 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 realised 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.
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关键词
resource allocation,wireless coexistence management system,wireless devices,licence-free bands,mutual interferences,central coexistence management system,conflict-free resource utilization,prospective medium utilisation,self learning concept,reinforcement learning agents,double deep Q-network,practically relevant coexistence scenarios
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