Resource Allocation for a Wireless Coexistence Management System Based on Reinforcement Learning

Philip Soeffker; Dimitri Block; Nico Wiebusch; Uwe Meier

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arXiv · 1806.04702

Resource Allocation for a Wireless Coexistence Management System Based on Reinforcement Learning

Philip Soeffker·Dimitri Block·Nico Wiebusch·Uwe Meier

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Abstract

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.

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BibTeX RIS

Philip Soeffker, Dimitri Block, Nico Wiebusch, Uwe Meier. 2018-05-24. Resource Allocation for a Wireless Coexistence Management System Based on Reinforcement Learning. https://arxiv.org/abs/1806.04702

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