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

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)...

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Bibliographic Details
Main Authors: Chaouki Ben Issaid, Carles Anton-Haro, Xavier Mestre, Mohamed-Slim Alouini
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
NOMA
multi-label classification
classifier chains
gradient-boosting decision trees
user clustering
Online Access:https://ieeexplore.ieee.org/document/9261336/

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