  This paper demonstrates the use of machine learning to detect the presence of
intermodulation interference across several wireless carriers. We show a
salient characteristic of intermodulation interference and propose a machine
learning based algorithm that detects the presence of intermodulation
interference through the use of supervised learning. This algorithm can use the
radio access network intelligent controller or the sixth generation of wireless
communication (6G) edge node as a means of computation. Our proposed algorithm
runs in linear time in the number of resource blocks, making it a suitable
radio resource management application in 6G.
