Abstract
The technology which utilizes 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 power line communication
systems.