Open AccessHigh‐resolution and low‐complexity dynamic topology estimation for PLC networks assisted by impulsive noise source detection
Author(s) -
Zhang Chao,
Zhu Xu,
Huang Yi,
Liu Gan
Publication year - 2016
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2015.0454
Subject(s) - computer science , noise (video) , topology (electrical circuits) , network topology , estimation , computer network , mathematics , artificial intelligence , engineering , combinatorics , image (mathematics) , systems engineering
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.