Document Type
Conference Item
Publication Date
7-16-2025
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
Keywords
6G networks, D2D communication, Energy efficiency, Machine Learning, Mode selection
Divisions
fac_eng
Event Title
The 2025 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)
Event Location
Bali, Indonesia
Event Dates
3-5 July 2025
Event Type
conference
Additional Information
Conference paper 2025