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Title: Learning-based energy-efficient resource management by heterogeneous RF/VLC for ultra-reliable low-latency industrial IoT networks
Authors: Yang, Helin
Alphones, Arokiaswami
Zhong, Wen-De
Chen, Chen
Xie, Xianzhong
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Yang, H., Alphones, A., Zhong, W.-D., Chen, C., & Xie, X. (2019). Learning-based energy-efficient resource management by heterogeneous RF/VLC for ultra-reliable low-latency industrial IoT networks. IEEE Transactions on Industrial Informatics, 16(8), 5565-5576. doi:10.1109/TII.2019.2933867
Journal: IEEE Transactions on Industrial Informatics
Abstract: Smart factory under Industry 4.0 and industrial Internet of Things (IoT) has attracted much attention from both academia and industry. In wireless industrial networks, industrial IoT and IoT devices have different quality-of-service (QoS) requirements, ranging from ultra-reliable low-latency communications (URLLC) to high transmission data rates. These industrial networks will be highly complex and heterogeneous, as well as the spectrum and energy resources are severely limited. Hence, this article presents a heterogeneous radio frequency (RF)/visible light communication (VLC) industrial network architecture to guarantee the different QoS requirements, where RF is capable of offering wide-area coverage and VLC has the ability to provide high transmission data rate. A joint uplink and downlink energy-efficient resource management decision-making problem (network selection, subchannel assignment, and power management) is formulated as a Markov decision process. In addition, a new deep post-decision state (PDS)-based experience replay and transfer (PDS-ERT) reinforcement learning algorithm is proposed to learn the optimal policy. Simulation results corroborate the superiority in performance of the presented heterogeneous network, and verify that the proposed PDS-ERT learning algorithm outperforms other existing algorithms in terms of meeting the energy efficiency and the QoS requirements.
ISSN: 1551-3203
DOI: 10.1109/TII.2019.2933867
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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