High-resolution and low-complexity dynamic topology estimation for PLC networks assisted by impulsive noise source detection

Chao Zhang; Xu Zhu; Yi Huang; Gan Liu

High-resolution and low-complexity dynamic topology estimation for PLC networks assisted by impulsive noise source detection

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Author(s): Chao Zhang ¹ ; Xu Zhu ¹ ; Yi Huang ¹ ; Gan Liu ²
- Affiliations: 1: Department of Electrical Engineering and Electronics, The University of Liverpool, Liverpool L69 3GJ, UK ;
  2: Department of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan, People's Republic of China
Source: Volume 10, Issue 4, 03 March 2016, p. 443 – 451
DOI: 10.1049/iet-com.2015.0454 , Print ISSN 1751-8628, Online ISSN 1751-8636

Received 13/05/2015, Accepted 10/01/2016, Revised 05/11/2015, Published 03/03/2016

Power line communication (PLC) network is a promising solution for smart grid and home broadband applications. However, its topology is not always available and is time-varying due to frequent load changes. The existing topology estimation approaches require installation of a large number of PLC modems or suffer limited resolution. Moreover, they are designed for static PLC networks and need to be repeated at a regular basis to combat topology changes. The authors propose a high-resolution and low-complexity dynamic topology estimation scheme for time-varying indoor PLC networks, which consists of three parts: (i) a time-frequency domain reflectometry based path length estimation method, which requires measurement at a single PLC modem and achieves a much higher resolution than the frequency domain reflectometry based method; (ii) a node-by-node greedy algorithm for topology reconstruction, which is much more computationally efficient than the existing peak-by-peak searching algorithm; (iii) an impulsive noise assisted dynamic topology re-estimation method, which results in a significant complexity reduction over fixed-frequency re-estimation.

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High-resolution and low-complexity dynamic topology estimation for PLC networks assisted by impulsive noise source detection

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