Tri-partition method for channel state detection in the power line communication systems

Zhen Wang; Chuan Liu; Wei Peng; Shidong Liu

doi:10.1117/12.3037482

27 September 2024 Tri-partition method for channel state detection in the power line communication systems

Zhen Wang, Chuan Liu, Wei Peng, Shidong Liu

Author Affiliations +

Proceedings Volume 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024); 132750N (2024) https://doi.org/10.1117/12.3037482
Event: 6th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 2024, Wuhan, China

Abstract

In power line communication system, ensuring support for multi-user concurrent communication entails the precise channel state detection to determine whether the channel is available. However, power line communication system is greatly affected by the impulse noise, which is a challenge not fully addressed by existing channel state detection schemes. To improve the accuracy of discerning the state of power line channels, unlike the traditional channel detection scheme that divides the channel state into two states: with signal or without signal, this paper categorizes channel states into three classifications: Signal, Impulse noise, and Gaussian noise. This classification is achieved through the application of deep learning. Our method leverages both time domain and frequency domain information of the signal to refine accuracy. Simulation results demonstrate a notable improvement in the accuracy of our proposed channel state detection method compared to conventional methods. Moreover, the proposed method can adapt to different signal modulation types, demonstrating strong robustness.

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

Citation Download Citation

Zhen Wang, Chuan Liu, Wei Peng, and Shidong Liu "Tri-partition method for channel state detection in the power line communication systems", Proc. SPIE 13275, Sixth International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), 132750N (27 September 2024); https://doi.org/10.1117/12.3037482

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