  The problem of relay selection is pivotal in the realm of cooperative
communication. However, this issue has not been thoroughly examined,
particularly when the background noise is assumed to possess an impulsive
characteristic with consistent memory as observed in smart grid communications
and some other wireless communication scenarios. In this paper, we investigate
the impact of this specific type of noise on the performance of cooperative
Wireless Sensor Networks (WSNs) with the Decode and Forward (DF) relaying
scheme, considering Symbol-Error-Rate (SER) and battery power consumption
fairness across all nodes as the performance metrics. We introduce two
innovative relay selection methods that depend on noise state detection and the
residual battery power of each relay. The first method encompasses the
adaptation of the Max-Min criterion to this specific context, whereas the
second employs Reinforcement Learning (RL) to surmount this challenge. Our
empirical outcomes demonstrate that the impacts of bursty impulsive noise on
the SER performance can be effectively mitigated and that a balance in battery
power consumption among all nodes can be established using the proposed
methods.
