A Multilayer Perceptron-Based Impulsive Noise Detector with Application to Power-Line-Based Sensor Networks

Ying-Ren Chien, Jie-Wei Chen, Sendren Sheng-Dong Xu

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

For 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.
Original languageEnglish (US)
Pages (from-to)21778-21787
Number of pages10
JournalIEEE Access
Volume6
DOIs
StatePublished - Apr 10 2018

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