Unsupervised Machine Learning-Based User Clustering in THz-NOMA Systems

Lin, Yushen; Wang, Kaidi; Ding, Zhiguo

doi:10.1109/lwc.2023.3262788

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…Table 7 provides a concise summary of the studies discussed in this subsection. Improved the inter-RAT handover success rate, kept the session in the optimal band, had a high chance of supporting the self-organizing network [127] Unsupervised ML-based user clustering algorithms…”

Section: Millimeter-wave and Terahertz Communicationsmentioning

confidence: 99%

“…Computational reduction [93] Spectral efficiency [93,115] Throughput improvement [127] ANN Collection of neurons at each layer with inputs working in the feed-forward structure…”

Section: Mobile Edge Computing (Mec)mentioning

confidence: 99%

“…Therefore, it likely has a high chance of supporting the self-organizing network regarding high availability, bandwidth, low latency, and reducing degraded service in a handover time. In [ 127 ], the authors applied three unsupervised learning algorithms to cluster secondary users without knowing the number of clusters and degrading the primary user’s performance. Three unsupervised ML algorithms, namely K-means, agglomerative hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN), were used in the THz-NOMA network.…”

Section: ML Algorithm Implementation For Emerging Technologies In a 6...mentioning

confidence: 99%

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Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Puspitasari,

An,

Alsharif

et al. 2023

Sensors

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The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

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Section: Millimeter-wave and Terahertz Communicationsmentioning

confidence: 99%

“…Computational reduction [93] Spectral efficiency [93,115] Throughput improvement [127] ANN Collection of neurons at each layer with inputs working in the feed-forward structure…”

Section: Mobile Edge Computing (Mec)mentioning

confidence: 99%

Section: ML Algorithm Implementation For Emerging Technologies In a 6...mentioning

confidence: 99%

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Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Puspitasari,

An,

Alsharif

et al. 2023

Sensors

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AI-Empowered Multiple Access for 6G: A Survey of Spectrum Sensing, Protocol Designs, and Optimizations

Cao,

Yang,

Wang

et al. 2024

Proc. IEEE

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Uplink Data Rate Maximization in Multi-Cell BackCom NOMA Systems

Lin,

Wang,

Wang

et al. 2024

IEEE Open J. Commun. Soc.

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This paper investigates a backscatter communication (BackCom) non-orthogonal multiple access (NOMA) system with numerous base stations (BSs) and backscatter devices (BDs). The objective is to maximize sum uplink rate by adjusting reflection coefficients of BDs and establishing association policies between BSs and BDs. To achieve this, matching theory is utilized to address the association problem, and a quadratic transform iterative algorithm is employed to calculate optimal reflection coefficients under a given association policy. Meanwhile, hybrid successive interference cancellation (SIC) is applied to reduce the outage probability of the proposed system while optimizing the uplink sum data rate, where a low-complexity solution is developed. Simulation results validate the superiority of the proposed optimization scheme in enhancing uplink sum data rate. The comparison between BackCom NOMA and BackCom orthogonal multiple access (OMA) reveals superior performance of the former, which becomes increasingly evident as the number of BSs and BDs increases. The proposed iterative algorithm significantly enhances the system's sum uplink data rate. The usage of hybrid SIC also notably declines the outage probability of the system.

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Unsupervised Machine Learning-Based User Clustering in THz-NOMA Systems

Cited by 8 publications

References 12 publications

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

AI-Empowered Multiple Access for 6G: A Survey of Spectrum Sensing, Protocol Designs, and Optimizations

Uplink Data Rate Maximization in Multi-Cell BackCom NOMA Systems

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