Unsupervised User Clustering in Non-orthogonal Multiple Access

Jie Ren, Zulin Wang, Mai Xu, Fang Fang, Zhiguo DIng

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

7 Scopus citations

Abstract

Non-orthogonal multiple access (NOMA) is one of the most promising technologies in fifth-generation mobile communication system for its advantages in serving multiuser simultaneously and enhancing spectrum efficiency. In this paper, we investigate the optimization problem of sum-rate maximization for NOMA-based system, and mainly focus on user clustering. Inspired by the correlation features of users, we introduce machine learning in user clustering. We first develop an expectation maximization (EM) based algorithm for fixed user scenario. Then, the dynamic user scenario is considered and an online EM (OLEM) based clustering algorithm is proposed. Simulation results show that the proposed EM-based and OLEM-based algorithms outperform the state-of-the-art algorithms in fixed and dynamic user scenario, respectively.

Original languageBritish English
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3332-3336
Number of pages5
ISBN (Electronic)9781479981311
DOIs
StatePublished - May 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

Keywords

  • expectation maximization
  • NOMA
  • online learning
  • unsupervised learning
  • user clustering

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