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

Chaouki Ben Issaid, Carles Antón-Haro, Xavier Mestre, Mohamed-Slim Alouini

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, 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.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE Access
DOIs
StatePublished - 2020

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