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 multicas...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.展开更多
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)+1 种基金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).
文摘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.
