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Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

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Alternative Title
Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems
Abstract
The performance of data aggregation in industrial wireless communications can be degraded by environmental interference on Industrial Scientific Medical (ISM) channels. In this paper, cognitive radio (CR) was applied to enable devices to share primary channels with the aim of enhancing the transmission performance of the WirelessHART network. We considered a linear convergecast system, where the packets generated at each device were routed to the gateway (GW) through the aid of neighboring devices. The solar-powered cognitive access points (CAPs) were deployed to improve the network performance by opportunistically allocating the primary channels to the devices for data transmissions. Firstly, we formulate the scheduling problem of long-term throughput maximization as a framework of a Markov decision process with the constraints of the minimum delay, the number of required ISM channels, and the harvested energy at the CAPs. Then, we propose a deep reinforcement learning-based scheduling scheme to optimally assign multiple ISM and primary channels to the field devices in each superframe. The simulation results confirmed the superiority of the proposed scheme compared to existing methods.
Author(s)
구인수쩐 눗 카이 호안팜 주이 탄황 티 흐엉 지앙
Issued Date
2021
Type
Article
Keyword
cognitive radioindustrial scientific medicalInterferenceJob shop schedulingMarkov decision processPerformance evaluationProtocolsSensorsWireless communicationWireless sensor networksWirelessHART
DOI
10.1109/ACCESS.2021.3123213
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9093
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_14e593c3f43f44b6be9771b424113c58&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Packet%20Delivery%20Maximization%20Using%20Deep%20Reinforcement%20Learning-Based%20Transmission%20Scheduling%20for%20Industrial%20Cognitive%20Radio%20Systems&offset=0&pcAvailability=true
Publisher
IEEE ACCESS
Location
미국
Language
영어
ISSN
2169-3536
Citation Volume
9
Citation Number
1
Citation Start Page
146492
Citation End Page
146508
Appears in Collections:
Engineering > IT Convergence
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