Clustering and Resource Allocation Strategy for D2D Multicast Networks with Machine Learning Approaches

作     者：Fan Jiang Lan Zhang Changyin Sun Zeng Yuan Fan Jiang;Lan Zhang;Changyin Sun;Zeng Yuan

作者机构：Xi’an University of Posts and TelecommunicationsXi’an 710121China Shaanxi Key Laboratory of Telecommunications and Information Networks and SecurityXi’an 710121China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2021年第18卷第1期

页      面：196-211页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 081104[工学-模式识别与智能系统] 0839[工学-网络空间安全] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was supported by the National Natural Science Foundation of China(Grant Nos.62071377,61801382,61901367) the Key Project of Natural Science Foundation of Shaanxi Province(Grant No.2019JZ-06) the Key Industrial Chain Project of Shaanxi Province(Grant No.2019ZDLGY07-06) the College Science and Technology Innovation Activity Project of Xi’an University of Posts and Telecommunications(Grant No.19-B-289). 

主　　题：device-to-device multicast communication clustering energy efficiency resource allocation Q-Learning 

摘      要：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.