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With the rapid development of future wireless communication, the combinationof NOMA technology and millimeter-wave(mmWave) technology has become a researchhotspot. The application of NOMA in mmWave heterogeneous networks can meet thediverse needs of users in different applications and scenarios in futurecommunications. In this paper, we propose a machine learning framework to dealwith the user association, subchannel and power allocation problems in such acomplex scenario. We focus on maximizing the energy efficiency (EE) of thesystem under the constraints of quality of service (QoS), interferencelimitation, and power limitation. Specifically, user association is solvedthrough the Lagrange dual decomposition method, while semi-supervised learningand deep neural network (DNN) are used for the subchannel and power allocation,respectively. In particular, unlabeled samples are introduced to improveapproximation and generalization ability for subchannel allocation. Thesimulation indicates that the proposed scheme can achieve higher EE with lowercomplexity.

Deep Learning based Radio Resource Management in NOMA Networks: User Association, Subchannel and Power Allocation