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• W3036545327 abstract "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." @default.
• W3036545327 created "2020-06-25" @default.
• W3036545327 creator A5029384171 @default.
• W3036545327 creator A5049518195 @default.
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• W3036545327 date "2020-06-20" @default.
• W3036545327 modified "2023-10-16" @default.
• W3036545327 title "Deep Learning based Radio Resource Management in NOMA Networks: User Association, Subchannel and Power Allocation" @default.
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• W3036545327 doi "https://doi.org/10.48550/arxiv.2006.11513" @default.
• W3036545327 hasPublicationYear "2020" @default.
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