Relay selection in cooperative power line communication: A multi-armed bandit approach

Power line communication (PLC) exploits the existence of installed infrastructure of power delivery system, in order to trans- mit 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 attenu- ation 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 trans- mitter and a receiver node, through a single array node which has to be selected from a set of available arrays. The relay selection problemcan be solved by having channel state information (CSI) at transmitter and selecting the relay which results in the best perfor- mance. However, acquiring the channel state information at trans- mitter 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 impul- sive noise of the PLC channel, in order to improve the relay selec- tion algorithm.