Abstract
The technology which utilises the power line as a medium for transferring information known as powerline communication (PLC) has been in existence for over a hundred years. It is beneficial because it avoids new installation since it uses the present installation for electrical power to transmit data. However, transmission of data signals through a power line channel usually experience some challenges which include impulsive noise, frequency selectivity, high channel attenuation, low line impedance, etc. The impulsive noise exhibits a power spectral density within the range of 10–15 dB higher than the background noise, which could cause a severe problem in a communication system. For better outcome of the PLC system, these noises must be detected and suppressed. This paper reviews various techniques used in detecting and mitigating the impulsive noise in PLC and suggests the application of machine learning algorithms for the detection and removal of impulsive noise in powerline communication systems.
| Original language | English |
|---|---|
| Pages (from-to) | 29-37 |
| Number of pages | 9 |
| Journal | Australian Journal of Electrical and Electronics Engineering |
| Volume | 15 |
| Issue number | 1-2 |
| DOIs | |
| Publication status | Published - 3 Apr 2018 |
Keywords
- Additive white Gaussian Noise (AWGN)
- Artificial Intelligence (AI)
- Impulsive Noise
- Machine Learning (ML) Techniques
- Orthogonal Frequency Division Multiplexing (OFDM)
- Power line communication (PLC)
ASJC Scopus subject areas
- Electrical and Electronic Engineering