  With outstanding features, Machine Learning (ML) has been the backbone of
numerous applications in wireless networks. However, the conventional ML
approaches have been facing many challenges in practical implementation, such
as the lack of labeled data, the constantly changing wireless environments, the
long training process, and the limited capacity of wireless devices. These
challenges, if not addressed, will impede the effectiveness and applicability
of ML in future wireless networks. To address these problems, Transfer Learning
(TL) has recently emerged to be a very promising solution. The core idea of TL
is to leverage and synthesize distilled knowledge from similar tasks as well as
from valuable experiences accumulated from the past to facilitate the learning
of new problems. Doing so, TL techniques can reduce the dependence on labeled
data, improve the learning speed, and enhance the ML methods' robustness to
different wireless environments. This article aims to provide a comprehensive
survey on applications of TL in wireless networks. Particularly, we first
provide an overview of TL including formal definitions, classification, and
various types of TL techniques. We then discuss diverse TL approaches proposed
to address emerging issues in wireless networks. The issues include spectrum
management, localization, signal recognition, security, human activity
recognition and caching, which are all important to next-generation networks
such as 5G and beyond. Finally, we highlight important challenges, open issues,
and future research directions of TL in future wireless networks.
