  Data-driven paradigms are well-known and salient demands of future wireless
communication. Empowered by big data and machine learning, next-generation
data-driven communication systems will be intelligent with the characteristics
of expressiveness, scalability, interpretability, and especially uncertainty
modeling, which can confidently involve diversified latent demands and
personalized services in the foreseeable future. In this paper, we review and
present a promising family of nonparametric Bayesian machine learning methods,
i.e., Gaussian processes (GPs), and their applications in wireless
communication due to their interpretable learning ability with uncertainty.
Specifically, we first envision three-level motivations of data-driven wireless
communication using GPs. Then, we provide the background of the GP model in
terms of covariance structure and model inference. The expressiveness of the GP
model is introduced by using various interpretable kernel designs, namely,
stationary, non-stationary, deep, and multi-task kernels. Furthermore, we
review the distributed GP with promising scalability, which is suitable for
applications in wireless networks with a large number of distributed edge
devices. Finally, we provide representative solutions and promising techniques
that adopting GPs in wireless communication systems.
