  Effective resource allocation plays a pivotal role for performance
optimization in wireless networks. Unfortunately, typical resource allocation
problems are mixed-integer nonlinear programming (MINLP) problems, which are
NP-hard. Machine learning based methods recently emerge as a disruptive way to
obtain near-optimal performance for MINLP problems with affordable
computational complexity. However, they suffer from severe performance
deterioration when the network parameters change, which commonly happens in
practice and can be characterized as the task mismatch issue. In this paper, we
propose a transfer learning method via self-imitation, to address this issue
for effective resource allocation in wireless networks. It is based on a
general "learning to optimize" framework for solving MINLP problems. A unique
advantage of the proposed method is that it can tackle the task mismatch issue
with a few additional unlabeled training samples, which is especially important
when transferring to large-size problems. Numerical experiments demonstrate
that with much less training time, the proposed method achieves comparable
performance with the model trained from scratch with sufficient amount of
labeled samples. To the best of our knowledge, this is the first work that
applies transfer learning for resource allocation in wireless networks.
