VIGAMP-based impulse noise estimation in power line communication

Mingfeng Cheng; Chuan Liu; Wei Peng; Lei Chen; Zhen Wang

doi:10.1117/12.3037513

27 September 2024 VIGAMP-based impulse noise estimation in power line communication

Mingfeng Cheng, Chuan Liu, Wei Peng, Lei Chen, Zhen Wang

Author Affiliations +

Proceedings Volume 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024); 132750O (2024) https://doi.org/10.1117/12.3037513
Event: 6th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 2024, Wuhan, China

Abstract

Power line communication (PLC) is essential for smart grid applications but struggles with noise due to its use of electrical lines for data transmission. PLC systems are particularly affected by bursty impulse noise. In order to estimate the impulse noise, this paper proposes a variational inference generalized approximate message passing (VIGAMP) based algorithm that simplifies complexity and improves performance for PLC-OFDM system. Compared to existing Sparse Bayesian Learning algorithms, Simulations reveal that the proposed method offers better Bit Error Rates performance and faster convergence.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingfeng Cheng, Chuan Liu, Wei Peng, Lei Chen, and Zhen Wang "VIGAMP-based impulse noise estimation in power line communication", Proc. SPIE 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 132750O (27 September 2024); https://doi.org/10.1117/12.3037513

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE