A Novel Approach for User Clustering In mmWave Non-Orthogonal Multiple Access System
DOI:
https://doi.org/10.63094/AITUSRJ.25.4.1.6Keywords:
mmWave, NOMA, K-means Clustering, Hierarchical Clustering, Unsupervised Machine LearningAbstract
Non-Orthogonal Multiple Access (NOMA) systems require user clustering to optimize resource allocation. In this paper, a clustering approach is developed to group users in Millimeter Wave Non-Orthogonal Multiple Access (mmWave-NOMA) systems. The proposed approach follows five main steps: data preparation, distance estimation, Signal-to-Interference-and-Noise Ratio (SINR) calculation, data combination, and normalization. To cluster users into optimal groups, a novel version of the K-means algorithm that incorporates both distance and SINR values is employed. The Bayesian Information Criterion (BIC) is utilized to determine the optimal number of clusters. The performance of the proposed method is evaluated based on the overall achievable sum rate. Experimental results show that the proposed method achieves a sum rate of 3 bps/Hz when the transmission power is set to 30 dBm and the number of users is 50, and 2.6 bps/Hz when the number of users is 500, respectively, indicating its effectiveness.
References
S. Al-Sarawi, M. Anbar, R. Abdullah, and A. B. Al Hawari, "Internet of things market analysis forecasts, 2020–2030," in Proc. World Conf. Smart Trends Syst., Secur. Sustain. (WS4), 2020, doi: 10.1109/WorldS450073.2020.9210375.
Cisco and S. Jose, "Cisco visual networking index (VNI) global mobile data traffic forecast update, 2017–2022 white paper," Cisco, USA, 2019.
W. Saad, M. Bennis, and M. Chen, "A vision of 6G wireless systems: Applications, trends, technologies, and open research problems," IEEE Netw., 2020, doi: 10.1109/MNET.001.1900287.
H. T. Hoang, Q. V. Pham, and W. J. Hwang, "Spatial-temporal-DBSCAN-based user clustering and power allocation for sum rate maximization in millimeter-wave NOMA systems," Symmetry, vol. 12, no. 11, p. 1854, 2020, doi: 10.3390/sym12111854.
K. T. Chui, M. D. Lytras, and A. Visvizi, "Energy sustainability in smart cities: Artificial intelligence, smart monitoring, and optimization of energy consumption," Energies, vol. 11, no. 11, p. 2869, 2018, doi: 10.3390/en11112869.
Z. Ding, P. Fan, and H. V. Poor, "Random beamforming in millimeter-wave NOMA networks," IEEE Access, 2017, doi: 10.1109/ACCESS.2017.2673248.
J. Choi, "NOMA: Principles and recent results," in Proc. Int. Symp. Wireless Commun. Syst., 2017, doi: 10.1109/ISWCS.2017.8108138.
O. Maraqa, A. S. Rajasekaran, S. Al-Ahmadi, H. Yanikomeroglu, and S. M. Sait, "A survey of rate-optimal power domain NOMA with enabling technologies of future wireless networks," IEEE Commun. Surveys Tuts., 2020, doi: 10.1109/COMST.2020.3013514.
T. P. Huynh, P. N. Son, and M. Voznak, "Secrecy performance of underlay cooperative cognitive network using non-orthogonal multiple access with opportunistic relay selection," Symmetry, vol. 11, no. 3, p. 385, 2019, doi: 10.3390/sym11030385.
J. Kim, J. Koh, J. Kang, K. Lee, and J. Kang, "Design of user clustering and precoding for downlink non-orthogonal multiple access (NOMA)," in Proc. IEEE Mil. Commun. Conf. (MILCOM), 2015, doi: 10.1109/MILCOM.2015.7357604.
M. S. Ali, H. Tabassum, and E. Hossain, "Dynamic user clustering and power allocation for uplink and downlink non-orthogonal multiple access (NOMA) systems," IEEE Access, 2016, doi: 10.1109/ACCESS.2016.2604821.
E. Bellodi, R. Zese, F. Riguzzi, and E. Lamma, "Introduction to machine learning," in Mach. Learn. Non-volatile Memories, 2022, doi: 10.1007/978-3-031-03841-9_1.
B. Di, L. Song, and Y. Li, "Sub-channel assignment, power allocation, and user scheduling for non-orthogonal multiple access networks," IEEE Trans. Wireless Commun., 2016, doi: 10.1109/TWC.2016.2606100.
S. M. Hamedoon, J. N. Chattha, and M. Bilal, "Towards intelligent user clustering techniques for non-orthogonal multiple access: a survey," EURASIP J. Wireless Commun. Netw., 2024, doi: 10.1186/s13638-024-02333-z.
J. Cui, Z. Ding, P. Fan, and N. Al-Dhahir, "Unsupervised machine learning-based user clustering in millimeter-wave-NOMA systems," IEEE Trans. Wireless Commun., 2018, doi: 10.1109/TWC.2018.2867180.
A. S. Rajasekaran and H. Yanikomeroglu, "Neural network aided user clustering in mmWave-NOMA systems with user decoding capability constraints," IEEE Access, 2023, doi: 10.1109/ACCESS.2023.3274556.
M. Elsayed and M. Erol-Kantarci, "Radio resource and beam management in 5G mmWave using clustering and deep reinforcement learning," in Proc. IEEE Global Commun. Conf. (GLOBECOM), 2020, doi: 10.1109/GLOBECOM42002.2020.9322401.
M. Shahjalal, M. F. Ahmed, M. M. Alam, M. H. Rahman, and Y. M. Jang, "Fuzzy C-means clustering-based mMIMO-NOMA downlink communication for 6G ultra-massive interconnectivity," in Proc. 3rd Int. Conf. Artif. Intell. Inf. Commun. (ICAIIC), 2021, doi: 10.1109/ICAIIC51459.2021.9415222.
D. Marasinghe, N. Jayaweera, N. Rajatheva, and M. Latva-Aho, "Hierarchical user clustering for mmWave-NOMA systems," in Proc. 2nd 6G Wireless Summit, 2020, doi: 10.1109/6GSUMMIT49458.2020.9083909.
J. Ren, Z. Wang, M. Xu, F. Fang, and Z. Ding, "An EM-based user clustering method in non-orthogonal multiple access," IEEE Trans. Commun., 2019, doi: 10.1109/TCOMM.2019.2945334.
M. Shahjalal, M. H. Rahman, M. O. Ali, B. D. Chung, and Y. M. Jang, "User clustering techniques for massive MIMO-NOMA enabled mmWave/THz communications in 6G," in Proc. Int. Conf. Ubiquitous Future Netw. (ICUFN), 2021, doi: 10.1109/ICUFN49451.2021.9528659.
A. S. Rajasekaran, O. Maraqa, H. U. Sokun, H. Yanikomeroglu, and S. Al-Ahmadi, "User clustering in mmWave-NOMA systems with user decoding capability constraints for B5G networks," IEEE Access, 2020, doi: 10.1109/ACCESS.2020.3039276.
Rajasekaran, NOMA Integrated with Enabling Technologies and Practical Challenges, Carleton Univ., 2023.
H. Sokun, A. S. Rajasekaran, O. Maraqa, H. Yanikomeroglu, and A.-A. Saad, "Deep learning-based user clustering in millimeter wave non-orthogonal multiple access communications," Google Patents, 2024.
A. Alkhateeb, G. Leus, and R. W. Heath, "Limited feedback hybrid precoding for multi-user millimeter wave systems," IEEE Trans. Wireless Commun., 2015, doi: 10.1109/TWC.2015.2455980.
O. El Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. W. Heath, "Spatially sparse precoding in millimeter wave MIMO systems," IEEE Trans. Wireless Commun., 2014, doi: 10.1109/TWC.2014.011714.130846.
G. Lee, Y. Sung, and J. Seo, "Randomly-directional beamforming in millimeter-wave multiuser MISO downlink," IEEE Trans. Wireless Commun., 2016, doi: 10.1109/TWC.2015.2483493.
T. S. Rappaport, E. Ben-Dor, J. N. Murdock, and Y. Qiao, "38 GHz and 60 GHz angle-dependent propagation for cellular and peer-to-peer wireless communications," in Proc. IEEE Int. Conf. Commun. (ICC), 2012, doi: 10.1109/ICC.2012.6363891.
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