User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees
KAUST DepartmentCommunication Theory Lab
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Permanent link to this recordhttp://hdl.handle.net/10754/666011
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AbstractIn 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.
CitationIssaid, C. B., Anton-Haro, C., Mestre, X., & Alouini, M.-S. (2020). User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees. IEEE Access, 1–1. doi:10.1109/access.2020.3038490
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