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Mitigating Impulsive Noise for Wavelet-OFDM Powerline Communication

Author

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  • Ying-Ren Chien

    (Department of Electrical Engineering, National Ilan University, Yilan 26047, Taiwan)

  • Hao-Chun Yu

    (LitePoint Technology, Ltd., Taipei 11492, Taiwan)

Abstract

Advanced metering infrastructure (AMI) is an important application of smart grid communication technology used for the remote monitoring and control of smart meters. Broadband powerline communication (BB-PLC) systems could perhaps be used for AMI; however, impulsive noise (IN) greatly degrades performance. In addition to the fast Fourier transform (FFT)-based orthogonal frequency-division multiplexing (OFDM), IEEE 1901 specifications have defined the other physical layer called wavelet-based OFDM. Even though many existing studies have reported the IN mitigation algorithms for the FFT-based OFDM system, these approaches may not directly apply to the wavelet-OFDM-based PLC systems. In this paper, we propose a robust receiver for PLC systems based on wavelet-OFDM. The proposed receiver comprises a pre-IN mitigation block, an adaptive inverse discrete wavelet transform, and an iterative IN reconstruction block. The iterative cancellation of strong IN samples leads to a gradual improvement in the quality of the received signal. Instead of using the frequency domain approach, we reduce the inter-dependency of the channel estimation and per-subchannel equalization by using the time domain signal processing. Besides, we apply variable step-size adaptive algorithms to reduce the impact of IN during the training processes for the channel estimator and per-subchannel equalizer. In accordance with IEEE 1901 specifications, we built a simulation environment to evaluate the effectiveness of the proposed method. Simulation results demonstrated that conventional blanking devices fall short in terms of IN mitigation, and that the proposed scheme is able to achieve performance values approaching those obtained in cases without IN.

Suggested Citation

  • Ying-Ren Chien & Hao-Chun Yu, 2019. "Mitigating Impulsive Noise for Wavelet-OFDM Powerline Communication," Energies, MDPI, vol. 12(8), pages 1-13, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:8:p:1567-:d:225816
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    References listed on IDEAS

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    1. Augustine Ikpehai & Bamidele Adebisi & Khaled M. Rabie, 2016. "Broadband PLC for Clustered Advanced Metering Infrastructure (AMI) Architecture," Energies, MDPI, vol. 9(7), pages 1-19, July.
    2. Dong Sik Kim & Beom Jin Chung & Young Mo Chung, 2019. "Statistical Learning for Service Quality Estimation in Broadband PLC AMI," Energies, MDPI, vol. 12(4), pages 1-20, February.
    3. Jianhua Zhang & Adarsh Hasandka & Jin Wei & S. M. Shafiul Alam & Tarek Elgindy & Anthony R. Florita & Bri-Mathias Hodge, 2018. "Hybrid Communication Architectures for Distributed Smart Grid Applications," Energies, MDPI, vol. 11(4), pages 1-16, April.
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    Cited by:

    1. Wesley Angelino de Souza & Fernando Deluno Garcia & Fernando Pinhabel Marafão & Luiz Carlos Pereira da Silva & Marcelo Godoy Simões, 2019. "Load Disaggregation Using Microscopic Power Features and Pattern Recognition," Energies, MDPI, vol. 12(14), pages 1-18, July.
    2. Giovanni Artale & Antonio Cataliotti & Valentina Cosentino & Dario Di Cara & Riccardo Fiorelli & Salvatore Guaiana & Nicola Panzavecchia & Giovanni Tinè, 2019. "A New Coupling Solution for G3-PLC Employment in MV Smart Grids," Energies, MDPI, vol. 12(13), pages 1-23, June.

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