Clustering and resource allocation strategy for D2D multicast networks with machine learning approaches

• Published in: China communications Vol. 18; no. 1; pp. 196 - 211
• Main Authors: Jiang, Fan, Zhang, Lan, Sun, Changyin, Yuan, Zeng
• Format: Journal Article
• Language: English
• Published: China Institute of Communications 01.01.2021; Xi'an University of Posts and Telecommunications,Xi'an 710121,China; Shaanxi Key Laboratory of Telecommunications and Information Networks and Security,Xi'an 710121,China
• Subjects: clustering; Clustering algorithms; Copper; Device-to-device communication; device-to-device multicast communication; energy efficiency; Multicast algorithms; Power control; Q-Learning; Quality of service; resource allocation; Resource management; Q-Learning; clustering; resource allocation; energy efficiency; device-to-device multicast communica-tion
• ISSN: 1673-5447
• DOI: 10.23919/JCC.2021.01.017

In this paper, the clustering and resource allocation problem in device-to-device (D2D) multicast transmission underlay cellular networks are investigated. For the sake of classifying D2D users into different D2D multicast clusters, a hybrid intelligent clustering strategy (HICS) based on unsupervised machine learning is proposed first. By maximizing the total energy efficiency of D2D multicast clusters, a joint resource allocation scheme is then presented. More specifically, the energy efficiency optimization problem is constructed under the quality of service (QoS) constraints. Since the joint optimization problem is non-convex, we transform the original problem into a mixed-integer programming problem according to the Dinkelbach algorithm. Furthermore, to avoid the high computational complexity inherent in the traditional resource allocation problem, a Q-Learning based joint resource allocation and power control algorithm is proposed. Numerical results reveal that the proposed algorithm achieves better energy efficiency in terms of throughput per energy consumption.