Enhanced Cell Clustering and Multicast Scheduling for Energy-Efficient 5G/B5G MBSFN Networks

The emergence of 5G and beyond 5G (5G/B5G) networks represents a pivotal advancement in mobile wireless communication, offering peak transmission rates up to 20 Gb/s. This unprecedented capacity enables a diverse range of broadband multimedia services, including IPTV and Voice/Video-over-IP applications. With the exponential growth in multimedia services, the likelihood of multiple user equipments (UEs) within the same geographic region subscribing to identical multimedia services has surged, leading to significant inefficiencies in bandwidth utilization and radio resource consumption. To address these challenges, the multicast broadcast single frequency network (MBSFN) mechanism has been introduced, which clusters multiple cells into a unified serving area. By employing coordinated multipoint transmission (CoMP), MBSFN facilitates the synchronous delivery of multicast data across multiple UEs, effectively mitigating intercell interference and enhancing overall system efficiency. This approach not only reduces the data reception time for UEs but also significantly conserves their energy consumption. Nevertheless, the optimal clustering of cells and scheduling of multicast transmissions remain open research problems. In this study, we tackle the dual challenges of cell clustering and multicast scheduling with the objective of minimizing UEs’ energy consumption, measured in terms of data reception time, while ensuring the stringent Quality of Service (QoS) requirements of multicast services. To this end, we propose a two-phase framework. The first phase identifies the effective cell clusters to serve as multicast areas, balancing resource efficiency with QoS constraints. The second phase leverages dynamic programming techniques to further optimize multicast scheduling within these areas, thereby minimizing UEs’ energy. Comprehensive simulation results demonstrate that the proposed scheme significantly enhances resource utilization and reduces UEs’ energy consumption, outperforming state-of-the-art approaches.