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

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 transmiss...

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Autores principales:	Pham Duy Thanh, Tran Nhut Khai Hoan, Hoang Thi Huong Giang, Insoo Koo
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	WirelessHART cognitive radio Markov decision process industrial scientific medical Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/14e593c3f43f44b6be9771b424113c58
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spelling	oai:doaj.org-article:14e593c3f43f44b6be9771b424113c582021-11-09T00:01:51ZPacket Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems2169-353610.1109/ACCESS.2021.3123213https://doaj.org/article/14e593c3f43f44b6be9771b424113c582021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9585683/https://doaj.org/toc/2169-3536The 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.Pham Duy ThanhTran Nhut Khai HoanHoang Thi Huong GiangInsoo KooIEEEarticleWirelessHARTcognitive radioMarkov decision processindustrial scientific medicalElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 146492-146508 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	WirelessHART cognitive radio Markov decision process industrial scientific medical Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	WirelessHART cognitive radio Markov decision process industrial scientific medical Electrical engineering. Electronics. Nuclear engineering TK1-9971 Pham Duy Thanh Tran Nhut Khai Hoan Hoang Thi Huong Giang Insoo Koo Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems
description	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.
format	article
author	Pham Duy Thanh Tran Nhut Khai Hoan Hoang Thi Huong Giang Insoo Koo
author_facet	Pham Duy Thanh Tran Nhut Khai Hoan Hoang Thi Huong Giang Insoo Koo
author_sort	Pham Duy Thanh
title	Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems
title_short	Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems
title_full	Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems
title_fullStr	Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems
title_full_unstemmed	Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems
title_sort	packet delivery maximization using deep reinforcement learning-based transmission scheduling for industrial cognitive radio systems
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/14e593c3f43f44b6be9771b424113c58
work_keys_str_mv	AT phamduythanh packetdeliverymaximizationusingdeepreinforcementlearningbasedtransmissionschedulingforindustrialcognitiveradiosystems AT trannhutkhaihoan packetdeliverymaximizationusingdeepreinforcementlearningbasedtransmissionschedulingforindustrialcognitiveradiosystems AT hoangthihuonggiang packetdeliverymaximizationusingdeepreinforcementlearningbasedtransmissionschedulingforindustrialcognitiveradiosystems AT insookoo packetdeliverymaximizationusingdeepreinforcementlearningbasedtransmissionschedulingforindustrialcognitiveradiosystems
_version_	1718441362801557504

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

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