Machine leaming-empowered mode selection for QoS improvement in D2D-assisted communication networks

Kamarul Ariffin, Noordin and Kaharudin, Dimyati and Effariza, Hanafi and Hafiz Muhammad Fahad, Noman and Nuha Adiba, Mohd Ridzuan (2025) Machine leaming-empowered mode selection for QoS improvement in D2D-assisted communication networks. In: The 2025 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), 3-5 July 2025, Bali, Indonesia.

Text (Conference paper) The 2025 IEEE International Conference.pdf - Published Version Download (2MB)

Abstract

Device-to-device (D2D) communication has the potential to enhance network efficiency in upcoming sixthgeneration (6G) wireless networks by enabling direct communication between users. However, in ultra-dense network environments, severe interference can degrade D2D performance, potentially leading to suboptimal Quality of Service (QoS) for some users. To address this challenge, we propose a machine learning-based approach for dynamic mode selection between D2D and cellular communication, aiming to improve network efficiency and ensure optimal user satisfaction. The proposed framework leverages clustering algorithms, including k-means and k-medoids, to segregate users based on channel gain, Signal-to-Interference-plus-Noise Ratio (SINR), and path loss. Furthermore, we employ binary classification models, including k-Nearest Neighbor (k-NN), Naive Bayes, and Support Vector Machine (SVM), to predict the optimal communication mode, using achievable data rate as the performance metric. Simulation results demonstrate that kmedoids clustering coupled with a cubic SVM classifier achieves the highest accuracy of 98.36%, leading to significant performance improvements. Moreover, k-medoids-based mode selection results in a 19.66% throughput increase and a 20.37% reduction in energy consumption, outperforming k-meansbased approaches

Item Type:	Conference or Workshop Item (Paper)
Funders:	UNSPECIFIED
Additional Information:	Conference paper 2025
Uncontrolled Keywords:	6G networks; D2D communication; Energy efficiency; Machine Learning; Mode selection
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Engineering
Depositing User:	Mr. Mohd Safri
Date Deposited:	15 Aug 2025 01:20
Last Modified:	15 Aug 2025 01:20
URI:	http://eprints.um.edu.my/id/eprint/51064

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