  Power line communication (PLC) exploits the existence of installed
infrastructure of power delivery system, in order to transmit data over power
lines. In PLC networks, different nodes of the network are interconnected via
power delivery transmission lines, and the data signal is flowing between them.
However, the attenuation and the harsh environment of the power line
communication channels, makes it difficult to establish a reliable
communication between two nodes of the network which are separated by a long
distance. Relaying and cooperative communication has been used to overcome this
problem. In this paper a two-hop cooperative PLC has been studied, where the
data is communicated between a transmitter and a receiver node, through a
single array node which has to be selected from a set of available arrays. The
relay selection problem can be solved by having channel state information (CSI)
at transmitter and selecting the relay which results in the best performance.
However, acquiring the channel state information at transmitter increases the
complexity of the communication system and introduces undesired overhead to the
system. We propose a class of machine learning schemes, namely multi-armed
bandit (MAB), to solve the relay selection problem without the knowledge of the
channel at the transmitter. Furthermore, we develop a new MAB algorithm which
exploits the periodicity of the synchronous impulsive noise of the PLC channel,
in order to improve the relay selection algorithm.
