Comparison of Supervised Learning Methods for Spatial User Clustering in Downlink NOMA
Article Sidebar
Read Counter : 105
Download : 25
Main Article Content
Abstract
The performance of Power Domain Non-Orthogonal Multiple Access (PD-NOMA) is affected by the performance of Successive Interference Cancellation (SIC) in decoding user data. The large number of users will cause error propagation in SIC, which results in decreased SIC performance. This research aims to optimize the performance of SIC in PD-NOMA by applying spatial concepts to classify users. This research applies various supervised machine learning classification algorithms, including Decision Tree, K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Random Forest, Logistic Regression, and Naive Bayes. The experimental results show that Random Forest achieves the highest accuracy in classifying users, followed by Decision Tree. In addition, in performance measurement using ROC (Receiver Operating characteristic) and AUC (Area under the Curve) curves, the Random Forest method achieved the best results. In terms of experimentation process time, a decision tree has a faster time compared to a random forest. Overall, the Random Forest algorithm is suitable for the task of user clustering in the context of PD-NOMA, which utilizes the spatial concept from user to base station (BS).
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- Author grant the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
References
Benjebbour, A. (2017). An Overview of Non-Orthogonal Multiple Access. ZTE Communications, 15, 21–30. https://doi.org/10.3969
Ding, Z., Schober, R., & Poor, H. V. (2016). A General MIMO Framework for NOMA Downlink and Uplink Transmission Based on Signal Alignment. IEEE Transactions on Wireless Communications, 15(6), 4438–4454. https://doi.org/10.1109/TWC.2016.2542066
Islam, S. M. R., Avazov, N., Dobre, O. A., & Kwak, K. S. (2017a). Power-Domain Non-Orthogonal Multiple Access (NOMA) in 5G Systems: Potentials and Challenges. IEEE Communications Surveys and Tutorials, 19(2), 721–742. https://doi.org/10.1109/COMST.2016.2621116
Oladipupo, T. (2010). Types of Machine Learning Algorithms. In Y. Zhang (Ed.), New Advances in Machine Learning (pp. 19–48). Intech Open. https://doi.org/10.5772/9385
Prabha Kumaresan, S., Tan, C. K., & Ng, Y. H. (2020). Efficient user clustering using a low-complexity artificial neural network (ANN) for 5G NOMA systems. IEEE Access, 8, 179307–179316. https://doi.org/10.1109/ACCESS.2020.3027777
Song, L., Li, Y., Ding, Z., & Poor, H. V. (2017). Resource Management in Non-Orthogonal Multiple Access Networks for 5G and beyond. IEEE Network, 31(4), 8–14. https://doi.org/10.1109/MNET.2017.1600287
You, H., Pan, Z., Liu, N., & You, X. (2020). User Clustering Scheme for Downlink Hybrid NOMA Systems Based on Genetic Algorithm. IEEE Access, 8, 129461–129468. https://doi.org/10.1109/ACCESS.2020.3009018
Cui, J., Khan, M. B., Deng, Y., Ding, Z., & Nailanathan, A. (2019). Unsupervised Learning Approaches for User Clustering in NOMA enabled Aerial SWIPT Networks. IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC, 2019-July(July). https://doi.org/10.1109/SPAWC.2019.8815399
Liaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3), 18–22.
Lin, Y., Wang, K., & Ding, Z. (2023). Unsupervised Machine Learning-Based User Clustering in THz-NOMA Systems. IEEE Wireless Communications Letters, 12(7), 1130–1134. https://doi.org/10.1109/LWC.2023.3262788
Rish I. (2001). An empirical study of the naive bayes classifier. IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, January 2001, 41–46. http://www.cc.gatech.edu/home/isbell/classes/reading/papers/Rish.pdf