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The latest technological developments have fueled revolutionary changes and improvements in wireless communication systems. Among them, mmWave spectrum exploitation stands out for its ability to deliver ultra-high data rates. However, its full adoption beyond fifth generation multicast systems (5G+/6G) remains hampered, mainly due to mobility robustness issues. In this work, we propose a solution to address the problem of efficient sidelink-assisted multicasting in mobile multimode systems, specifically by considering the possibility of jointly utilizing sidelink/device-to-device (D2D), unicast, and multicast transmissions to improve service delivery. To overcome the complexity problem in finding the optimal solution for user-mode binding, we introduce a pre-optimization step called multicast group formation (MGF). Through a clustering technique based on unsupervised machine learning, MGF allows to reduce the complexity of solving the sidelink-assisted multiple modes mmWave (SA3M) problem. A detailed analysis of the impact of various system parameters on performance is conducted, and numerical evidence of the complexity/performance trade-off and its dependence on mobility patterns and user distribution is provided. Particularly, our proposed solution achieves a network throughput improvement of up to 32% over state-of-the-art schemes while ensuring the lowest computational time. Finally, the results demonstrate that an effective balance between power consumption and latency can be achieved through appropriate adjustments of transmit power and bandwidth.

The design of multicast services in the fifth-generation (5G) New Radio (NR) deployments is hampered by the directional nature of antenna radiation patterns. This complexity is further compounded by the emergence of new deployment options, such as dual millimeter wave (mmWave) and microwave $(\mu $ Wave) base station (BS) deployments, as well as new antenna design solutions. In this paper, the resource allocation task for multicast services in dual mmWave/ $\mu $ Wave deployments with multi-beam directional antennas is addressed as a multi-period variable cost and size bin packing problem. We solve this problem and characterize the globally optimal solution. To decrease complexity, we then propose and test the simulated annealing approximation and relaxation techniques, i.e., local branching and relaxation-induced neighborhood search heuristic. Our results show that for the considered system parameters, the properties of the optimal solution depend on the density of dual-mode BS deployment and BS deployment type. We observe a transition point at which the system shifts from primarily utilizing mmWave resources to exclusively using $\mu $ Wave BS. Furthermore, the optimal number of beams is upper limited by 3 for mmWave and by 2 for $\mu $ Wave BSs. The efficiency of resource utilization is also affected by the utilized numerology and technology selection priority. Finally, we show that the simulated annealing technique allows for decreasing the solution complexity at the expense of slightly overestimating the amount of resources.

The support of multicast communications in the fifth-generation (5G) New Radio (NR) system poses unique challenges to system designers. Particularly, the highly directional antennas do not allow to serve all the user equipment devices (UEs) that belong to the same multicast session in a single transmission. The capability of modern antenna arrays to utilize multiple beams simultaneously, with potentially varying half-power beamwidth, adds a new degree of freedom to the UE scheduling. This work addresses the challenge of optimal multicasting in 5G millimeter wave (mmWave) systems by presenting a globally optimal solution for multi-beam antenna operation. The optimization problem is formulated as a special case of multi-period variable cost and size bin packing problem that allows to not impose any constraints on the number of the beams and their configurations. We also propose heuristic solutions having polynomial time complexity. Our results show that for small cell radii of up to 100 meters, a single beam is always utilized. For higher cell coverage and practical ranges of the number of users (5-50), the optimal number of beams is upper bounded by 3.