The extent to which wireless technologies in license-free bands are used comes along with a decrease in performance due to mutual interference. This problem can be solved by implementation of an ...automated coexistence management that distributes the resources space, time and frequency. This can be done by means of an optimization algorithm that is able to find a global optimum in a large finite solution set. We implemented such an algorithm based upon evolutionary algorithms (EA). Utilizing this algorithm, we can repeatedly optimize the resource distribution. The wireless infrastructure is represented through a graph, where edges constitute the interferences.
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 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 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.
Wireless technologies in licence-free bands are already used to an extend, where mutual interference decreases the performance. Therefore, an automated coexistence management shall be implemented, ...which distributes the resources space, time and frequency in an optimal manner. Firstly, we define appropriate quality-of-coexistence (QoC) parameters which are determined by the distributed wireless systems and transmitted to a central coordination point (CCP). Secondly, we introduce an algorithm where the resources space, frequency, and time are considered as discrete variables. Additionally, channel sensing provides important information for the allocation decision by the CCP. The purpose of the concept is a resource allocation, which reduces the likelihood of interferences by considering all resources.
Wireless control systems for factory automation (FA) applications are subject to coexistence impairments, especially in license-free spectrum bands. Evaluating the coexistence impact requires the ...knowledge of appropriate characteristic parameters and the usage of a suitable simulation method. In this paper we propose an integral approach for the event-based simulation of wireless coexisting close-loop networked control systems (NCSs) for mutual impact evaluations based upon the integration of an ordinary differential equation resolving library within an event-based wireless network simulation framework. The approach is evaluated within a harsh FA scenario of two identical closed-loop NCS with two coexisting wireless technologies IEEE 802.11 and PNO WSAN-FA.
Wireless technologies in licence-free bands are widely used to an extent, where mutual interference decreases the performance. Therefore, a coexistence management shall be implemented, which ...distributes the resources space, time and frequency in an optimal manner. In this paper we purpose a centralized coexistence management concept, which is called central coordination point (CCP). This is based on SNMP (Simple Network Management Protocol) as a management protocol and real-time Ethernet as a control channel. Those technologies are common in industrial wireless communication systems. Furthermore, we suggest a wireless coexistence MIB (Management Information Base), which can easily be implemented in such wireless communication systems. In addition, we designed an algorithm for automatic resource allocation.
Towards Validation of Wireless Coexistence Management Willmann, Sarah; Meier, Marco; Rauchhaupt, Lutz ...
IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society,
2019-Oct., Letnik:
1
Conference Proceeding
The management of radio resources is an important task, especially in industrial wireless applications, to ensure dependable communication. Spectrum sensing, the classification of users and the type ...of industrial application as well as the development of appropriate resource allocation measures are subject of research. Evaluating the benefits of these measures cannot be achieved by simulations or measurements in real environments. The use in real systems is also not a suitable means for targeted analysis. This paper presents an approach for validating the individual functions of an automated coexistence management. The validation procedure is described with the help of concrete coexistence management functions. This concerns the concepts, the implementations and the execution of tests. The goal is to provide a validation platform for reproducible and standardisable tests to support the development of methods and algorithms for automated coexistence management solutions.
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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