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

Autor: Nico Wiebusch, Uwe Meier, Philip Soffker, Dimitri Block
Rok vydání: 2018
Předmět:
Signal Processing (eess.SP)
FOS: Computer and information sciences
Computer science
Distributed computing
Machine Learning (stat.ML)
02 engineering and technology
Machine Learning (cs.LG)
Computer Science - Networking and Internet Architecture
Statistics - Machine Learning
Wireless lan
0202 electrical engineering
electronic engineering
information engineering
FOS: Electrical engineering
electronic engineering
information engineering
Wireless
Reinforcement learning
Resource management
Electrical Engineering and Systems Science - Signal Processing
Networking and Internet Architecture (cs.NI)
Artificial neural network
business.industry
020206 networking & telecommunications
Computer Science - Learning
Management system
Resource allocation
020201 artificial intelligence & image processing
State (computer science)
business
Zdroj: ETFA
DOI: 10.48550/arxiv.1806.04702
Popis: 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)
Databáze: OpenAIRE
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