This research studies interpretability methods for deep neural network--based radio receivers in future wireless communication systems. The work focuses on representation-level analysis and training data attribution techniques that reveal how models encode physical-layer channel conditions and how training data influence receiver behavior. These insights enable validation, adaptation, and more trustworthy deployment of data-driven communication systems under realistic channel conditions, while providing deeper understanding of model behavior.