An Evolution-Affinity Propagation-Based Global Rate Maximization Clustering Policy in D2D Multicast Communication

In the coming 6G area, the throughput burden of cellular networks is generated by the the explosive growth in the number of smart devices and its requirements. The throughput burden can be mitigated effectively by the Device-to-Device Mul-ticast (D2MD) communication technology-based content sharing. With the aim of gaining the benefit of content sharing of D2MD, it is necessary to adopt an appropriate method for cluster formation that considers adaptivity and comprehensive influence. Thus, to address this method, this paper designs a global rate maximization problem by comprehensively taking into account the impact factors of the physical and social layers. Due to the high complexity of the designed problem, an Evolution-Affinity Propagation (EAP) algorithm is proposed to solve it. Finally, the performance of EAP is verified in simulation results and shows that the performance of EAP is improved by 2.5 times compared with spectral clustering.