Earticle

초록

영어

This paper presents a novel methodology to significantly enhance the reliability of high-speed power line communication (HS-PLC) systems via deep learning (DL). We impact to propose and validate a DL-based pre-compensation scheme specially designed for the effective mitigation of impulsive noise. It utilizes a pre-trained DL model deployed at the transmitter to accurately predict the instantaneous statistical characteristics of the impulsive noise. This predictive information enables real-time pre-compensation of the transmitted signal, resulting in a substantial improvement in the received signal quality. To ensure optimal prediction accuracy, a comprehensive noise database was meticulously constructed based on the empirical characteristics of measured noise patterns. For channel modeling, the Middleton Class A interference model was adopted to accurately simulate the representative impulsive noise conditions. The performance was rigorously evaluated through bit error rate (BER) analysis. Simulation results demonstrate that the proposed DL-based technique achieves a marked reduction in BER and a significant enhancement in signal quality relative to conventional systems. The developed system model holds promising potential as a universal solution for signal integrity improvement, extending its applicability beyond HS-PLC to a wide spectrum of wired and wireless communication systems susceptible to impulsive interference.

목차

Abstract
1. Introduction
2. PLC Channel Model
3. Data-Driven Interference Cancellation Scheme
4. Simulation Results
5. Conclusion
Reference

키워드

저자

• Sung-Il Seo [ Associate Professor, Department of Electrical Engineering, Honam University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

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• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380