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)...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9261336/ |
| _version_ | 1818847059199918080 |
|---|---|
| author | Chaouki Ben Issaid Carles Anton-Haro Xavier Mestre Mohamed-Slim Alouini |
| author_facet | Chaouki Ben Issaid Carles Anton-Haro Xavier Mestre Mohamed-Slim Alouini |
| author_sort | Chaouki Ben Issaid |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T05:55:25Z |
| format | Article |
| id | doaj.art-c7ab43040e344f30ba15dc84f1cc0942 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T05:55:25Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c7ab43040e344f30ba15dc84f1cc09422022-12-21T20:33:28ZengIEEEIEEE Access2169-35362020-01-01821141121142110.1109/ACCESS.2020.30384909261336User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision TreesChaouki Ben Issaid0https://orcid.org/0000-0002-4481-8168Carles Anton-Haro1https://orcid.org/0000-0002-5961-7692Xavier Mestre2Mohamed-Slim Alouini3https://orcid.org/0000-0003-4827-1793Centre for Wireless Communications (CWC), University of Oulu, Oulu, FinlandCentre Tecnològic de Telecomunicacions de Catalunya (CTTC/iCERCA), Parc Mediterrani Tecnologia (PMT), Castelldefels, SpainCentre Tecnològic de Telecomunicacions de Catalunya (CTTC/iCERCA), Parc Mediterrani Tecnologia (PMT), Castelldefels, SpainComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi ArabiaIn 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.https://ieeexplore.ieee.org/document/9261336/NOMAmulti-label classificationclassifier chainsgradient-boosting decision treesuser clustering |
| spellingShingle | Chaouki Ben Issaid Carles Anton-Haro Xavier Mestre Mohamed-Slim Alouini User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees IEEE Access NOMA multi-label classification classifier chains gradient-boosting decision trees user clustering |
| title | User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees |
| title_full | User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees |
| title_fullStr | User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees |
| title_full_unstemmed | User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees |
| title_short | User Clustering for MIMO NOMA via Classifier Chains and Gradient-Boosting Decision Trees |
| title_sort | user clustering for mimo noma via classifier chains and gradient boosting decision trees |
| topic | NOMA multi-label classification classifier chains gradient-boosting decision trees user clustering |
| url | https://ieeexplore.ieee.org/document/9261336/ |
| work_keys_str_mv | AT chaoukibenissaid userclusteringformimonomaviaclassifierchainsandgradientboostingdecisiontrees AT carlesantonharo userclusteringformimonomaviaclassifierchainsandgradientboostingdecisiontrees AT xaviermestre userclusteringformimonomaviaclassifierchainsandgradientboostingdecisiontrees AT mohamedslimalouini userclusteringformimonomaviaclassifierchainsandgradientboostingdecisiontrees |