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

• Published in: IEEE transactions on network science and engineering Vol. 7; no. 4; pp. 2406 - 2415
• Main Authors: Zhang, Haijun, Zhang, Haisen, Long, Keping, Karagiannidis, George K.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Complexity; Deep learning; energy efficiency; Heterogeneous networks; Machine learning; Millimeter waves; NOMA; Optimization; Quality of service; Resource management; semi-supervised learning; Wireless communication; Wireless communications
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2020.3004333

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.