Browse > Article
http://dx.doi.org/10.7236/JIIBC.2022.22.4.29

Cancellation Scheme of impusive Noise based on Deep Learning in Power Line Communication System  

Seo, Sung-Il (Dept. of Electrical Engineering, Honam University)
Publication Information
The Journal of the Institute of Internet, Broadcasting and Communication / v.22, no.4, 2022 , pp. 29-33 More about this Journal
Abstract
In this paper, we propose the deep learning based pre interference cancellation scheme algorithm for power line communication (PLC) systems in smart grid. The proposed scheme estimates the channel noise information by applying a deep learning model at the transmitter. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the power line communication performance is effectively removed through interference cancellation technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the theoretical model based on additive white Gaussian noise. As a result, the proposed interference cancellation with deep learning improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC for smart grid and general communication systems.
Keywords
Power Line Communication; Smart Grid System; Interference Cancellation; Impulsive Noise; Deep Learning; Channel Estimation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 M. S. Yousuf, M. El-Shafei, "Power line communications: an overview - part I," in proc. of 4th Conf. Innovations in Inf. Technol. '07, pp. 218-222, Nov. 1996. DOI: https://doi.org/10.1109/IIT.2007.4430363   DOI
2 S. Kim, T. Jeon, "Effect of Interference from DC power supply on power line communication channel," The Journal of The Institute of Internet, Broadcasting and Communication (JIIBC), Vol. 14, No. 5, pp. 111-115, Oct. 2014. DOI: https://doi.org/10.7236/JIIBC.2014.14.5.111   DOI
3 F. J. Canete, J. A. Cortes, L. Diez, J. T. Entrambasaguas, "Modeling and evaluation of the indoor power line transmission medium," IEEE Comm. Mag. Vol. 41, No. 4, pp. 41-47, Apr. 2003. DOI: https://doi.org/0.1109/MCOM.2003.1193973   DOI
4 H. Ye, G. Y. Li, B. H. Juang, "Power of deep learning for channel estimation and signal detection in OFDM systems," IEEE Wireless Communications Letters, Vol. 7, No. 1, pp. 114-117, Feb. 2018. DOI: https://doi.org/10.1109/LWC.2017.2757490   DOI
5 Y. Kim, S. Lee, Y. Kwon, "Proposal of step-by-step optimized campus power forecast model using CNN-LSTM deep learning ," Journal of the Korea Academia-Industrial cooperation Society(JKAIS), Vol. 21, No. 10, pp. 8-15, Oct, 2020. DOI: https://doi.org/10.5762/KAIS.2020.21.10.8   DOI
6 L. T. Tang, P. L. So, E. Gunawan, Y. L. Guan, S. Chen, T. T. Lie, "Characterization and modeling of in-building power lines for high-speed data transmission," IEEE Trans. Power Delivery, Vol. 18, pp. 69-77, Jan. 2003. DOI: https://doi.org/10.1109/TPWRD.2002.803796   DOI
7 C. B. Kim, "Deep learning model for prediction rate improvement of weather date using parallel merge structure," The Journal of KIIT, Vol. 20, No. 4, pp. 131-140, Apr. 2022. DOI: https://doi.org/10.14801/jkiit.2022.20.4.131   DOI
8 M. Zimmermann, K. Dostert, "Analysis and modeling of impulsive noise in broad band powerline communications," IEEE Trans. on Electromagnetic Compatibility, Vol. 44, No. 1, pp. 249-258, Feb. 2002. DOI: https://doi.org/10.1109/15.990732   DOI