Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning based Radio Resource Management in NOMA Networks: User
  Association, Subchannel and Power Allocation   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.

27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th