  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 a
promising family of nonparametric Bayesian machine learning methods, i.e.,
Gaussian processes (GPs), and their applications in wireless communication.
Since GPs achieve the expressive and interpretable learning ability with
uncertainty, it is particularly suitable for wireless communication. Moreover,
it provides a natural framework for collaborating data and empirical models
(DEM). Specifically, we first envision three-level motivations of data-driven
wireless communication using GPs. Then, we present the background of the GPs in
terms of covariance structure and model inference. The expressiveness of the GP
model using various interpretable kernel designs is surveyed, namely,
stationary, non-stationary, deep, and multi-task kernels. Furthermore, we
review the distributed GPs with promising scalability, which is suitable for
applications in wireless networks with a large number of distributed edge
devices. Finally, we list representative solutions and promising techniques
that adopt GPs in wireless communication systems.
