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dc.contributor.authorChien, Ying-Ren
dc.contributor.authorChen, Jie-Wei
dc.contributor.authorXu, Sendren Sheng-Dong
dc.date.accessioned2018-04-24T06:46:14Z
dc.date.available2018-04-24T06:46:14Z
dc.date.issued2018-04-10
dc.identifier.citationChien Y-R, Chen J-W, Xu SS-D (2018) A Multilayer Perceptron-Based Impulsive Noise Detector with Application to Power-Line-Based Sensor Networks. IEEE Access: 1–1. Available: http://dx.doi.org/10.1109/ACCESS.2018.2825239.
dc.identifier.issn2169-3536
dc.identifier.doi10.1109/ACCESS.2018.2825239
dc.identifier.urihttp://hdl.handle.net/10754/627597
dc.description.abstractFor power-line-based sensor networks, impulsive noise (IN) will dramatically degrade the data transmission rate in the power line. In this paper, we present a multilayer perceptron (MLP)-based approach to detect IN in orthogonal frequency-division multiplexing (OFDM)-based baseband power line communications (PLCs). Combining the MLP-based IN detection method with the outlier detection theory allows more accurate identification of the harmful residual IN. For OFDM-based PLC systems, the high peak-to-average power ratio (PAPR) of the received signal makes detection of harmful residual IN more challenging. The detection mechanism works in an iterative receiver that contains a pre-IN mitigation and a post-IN mitigation. The pre-IN mitigation is meant to null the stronger portion of IN, while the post-IN mitigation suppresses the residual portion of IN using an iterative process. Compared with previously reported IN detectors, the simulation results show that our MLP-based IN detector improves the resulting bit error rate (BER) performance.
dc.description.sponsorshipThis work was supported in part by the Ministry of Science and Technology (MOST), Taiwan, under the Grants MOST 103-2221-E-197-010 and MOST 106-2221-E-011-083.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8334525/
dc.rights(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
dc.subjectAnomaly detection
dc.subjectartificial neural networks (ANNs)
dc.subjectBit error rate
dc.subjectDetectors
dc.subjectImpulsive noise (IN)
dc.subjectIndexes
dc.subjectiterative algorithm
dc.subjectmultilayer perceptrons (MLPs)
dc.subjectPeak to average power ratio
dc.subjectPLC-based sensor networks
dc.subjectpower line communications (PLCs)
dc.subjectReceivers
dc.titleA Multilayer Perceptron-Based Impulsive Noise Detector with Application to Power-Line-Based Sensor Networks
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalIEEE Access
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Electrical Engineering, National Ilan University, Yilan 26047, Taiwan.
dc.contributor.institutionGraduate Institute of Automation and Control, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
kaust.personChen, Jie-Wei
refterms.dateFOA2018-06-14T04:24:52Z


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