  With the rapid development of future wireless communication, the combination
of NOMA technology and millimeter-wave(mmWave) technology has become a research
hotspot. The application of NOMA in mmWave heterogeneous networks can meet the
diverse needs of users in different applications and scenarios in future
communications. In this paper, we propose a machine learning framework to deal
with the user association, subchannel and power allocation problems in such a
complex scenario. We focus on maximizing the energy efficiency (EE) of the
system under the constraints of quality of service (QoS), interference
limitation, and power limitation. Specifically, user association is solved
through the Lagrange dual decomposition method, while semi-supervised learning
and deep neural network (DNN) are used for the subchannel and power allocation,
respectively. In particular, unlabeled samples are introduced to improve
approximation and generalization ability for subchannel allocation. The
simulation indicates that the proposed scheme can achieve higher EE with lower
complexity.
