User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees
Por:
Issaid, C.B., Anton-Haro, C., Mestre, X, Alouini M.-S.
Publicada:
1 ene 2020
Resumen:
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.
Filiaciones:
Issaid, C.B.:
Centre for Wireless Communications (CWC), University of Oulu, Oulu, 90570, Finland
Anton-Haro, C.:
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/iCERCA), Parc Mediterrani Tecnologia (PMT), Castelldefels, 08860, Spain
Ctr Tecnol Telecomunicac Catalunya CTTC iCERCA, Parc Mediterrani Tecnol PMT, Castelldefels 08860, Spain
Mestre, X:
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/iCERCA), Parc Mediterrani Tecnologia (PMT), Castelldefels, 08860, Spain
Ctr Tecnol Telecomunicac Catalunya CTTC iCERCA, Parc Mediterrani Tecnol PMT, Castelldefels 08860, Spain
Alouini M.-S.:
Computer Electrical and Mathematical Science and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
Univ Oulu, Ctr Wireless Commun CWC, Oulu 90570, Finland
King Abdullah Univ Sci & Technol KAUST, Comp Elect & Math Sci & Engn CEMSE Div, Thuwal 23955, Saudi Arabia
Green Submitted, gold, All Open Access; Gold Open Access; Green Open Access
|