@inproceedings{3db8d347381641069c46eabb36f1e550,
title = "Unsupervised User Clustering in Non-orthogonal Multiple Access",
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.",
keywords = "expectation maximization, NOMA, online learning, unsupervised learning, user clustering",
author = "Jie Ren and Zulin Wang and Mai Xu and Fang Fang and Zhiguo DIng",
note = "Funding Information: This work was supported by NSFC under Grants 61471022, 61573037 and 61876013, and by the Fok Ying Tung Education Foundation under Grant 151061. Mai Xu is the corresponding author of this paper. Publisher Copyright: {\textcopyright} 2019 IEEE.; 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",
year = "2019",
month = may,
doi = "10.1109/ICASSP.2019.8682205",
language = "British English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3332--3336",
booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",
address = "United States",
}