The Use of Machine Learning Techniques for Optimal Multicasting in 5G NR Systems

Multicasting is a key feature of cellular systems, which provides an efficient way to simultaneously disseminate
a large amount of traffic to multiple subscribers. However, the efficient use of multicast services in fifth-generation (5G)
New Radio (NR) is complicated by several factors, including inherent base station (BS) antenna directivity as well as the
exploitation of antenna arrays capable of creating multiple beams concurrently. In this work, we first demonstrate that the problem
of efficient multicasting in 5G NR systems can be formalized as a special case of multi-period variable cost and size bin
packing problem (BPP). However, the problem is known to be NP-hard, and the solution time is practically unacceptable for
large multicast group sizes. To this aim, we further develop and test several machine learning alternatives to address this
issue. The numerical analysis shows that there is a trade-off between accuracy and computational complexity for multicast
grouping when using decision tree-based algorithms. A higher number of splits offers better performance at the cost of an
increased computational time. We also show that the nature of the cell coverage brings three possible solutions to the multicast
grouping problem: (i) small-range radii are characterized by a single multicast subgroup with wide beamwidth, (ii) middle-
range deployments have to be solved by employing the proposed algorithms, and (iii) BS at long-range radii sweeps narrow unicast
beams to serve multicast users.