University of Oulu

C. Ben Issaid, C. Antón-Haro, X. Mestre and M. -S. Alouini, "User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees," in IEEE Access, vol. 8, pp. 211411-211421, 2020, doi: 10.1109/ACCESS.2020.3038490

User clustering for MIMO NOMA via classifier chains and gradient-boosting decision trees

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Author: Issaid, Chaouki Ben1; Antón-Haro, Charles2; Mestre, Xavier2;
Organizations: 1Centre for Wireless Communications (CWC), University of Oulu, 90570 Oulu, Finland
2Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/iCERCA), Parc Mediterrani Tecnologia (PMT), 08860 Castelldefels, Spain
3Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.1 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-01-18


In this article, we propose a data-driven approach to group users in a Non-Orthogonal Multiple Access (NOMA) MIMO setting. Specifically, we formulate user clustering as a multi-label classification problem and solve it by coupling a Classifier Chain (CC) with a Gradient Boosting Decision Tree (GBDT), namely, the LightGBM algorithm. The performance of the proposed CC-LightGBM scheme is assessed via numerical simulations. For benchmarking, we consider two classical adaptation learning schemes: Multi-Label k-Nearest Neighbours (ML-KNN) and Multi-Label Twin Support Vector Machines (ML-TSVM); as well as other naive approaches. Besides, we also compare the computational complexity of the proposed scheme with those of the aforementioned benchmarks.

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Series: IEEE access
ISSN: 2169-3536
ISSN-E: 2169-3536
ISSN-L: 2169-3536
Volume: 8
Pages: 211411 - 211421
DOI: 10.1109/ACCESS.2020.3038490
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the Spanish Government through the Aprendizaje Estadístico e Inferencia para Sistemas de Comunicación de Alta Dimensionalidad (ARISTIDES) project under Grant RTI2018-099722-B-I00 and in part by the King Abdullah University of Science and Technology (KAUST).
Copyright information: © The Authors 2020. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.For more information, see