User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees

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.

Authors:
Issaid Chaouki Ben, Antón-Haro Charles, Mestre Xavier, Alouini Mohamed-Slim

Publication type:
A1 Journal article – refereed

Place of publication:

Keywords:
classifier chains, gradient-boosting decision trees, multi-label classification, NOMA, user clustering

Published:

Full citation:
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

DOI:
https://doi.org/10.1109/ACCESS.2020.3038490

Read the publication here:
http://urn.fi/urn:nbn:fi-fe202101181991