The Application of Machine Learning in mmWave-NOMA Systems

Jingjing Cui, Zhiguo Ding, Pingzhi Fan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Scopus citations


Machine learning has been used to develop efficiently optimizing algorithms for practical communication systems. This paper investigates the user clustering and power allocation problem in the millimeter wave non- orthogonal multiple access (mmWave-NOMA) transmission scenario, where we assume that the users' locations of different clusters follows a Poisson cluster process (PCP). Specifically, we develop a machine learning based user clustering algorithm for the application of NOMA. Moreover, to investigate the performance of the proposed mmWave-NOMA system, we derive the optimal power allocation coefficients in closed-form by assuming equal power on each beam. In the simulation results, we firstly investigate the impact of the number of clusters on the system performance. We further show the validation of the proposed machine-learning based user clustering algorithm in the mmWave-NOMA system.

Original languageBritish English
Title of host publication2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538663554
StatePublished - 20 Jul 2018
Event87th IEEE Vehicular Technology Conference, VTC Spring 2018 - Porto, Portugal
Duration: 3 Jun 20186 Jun 2018

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference87th IEEE Vehicular Technology Conference, VTC Spring 2018


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