Multilink Solution for Seamless Transition Between Multicast and Unicast Areas in 5G Core Network

Abstract

A multilink (ML) methodology efficiently combines unicast (UC) and multicast (MC) transmissions to provide service to numerous devices. In this paper, the appropriate use of ML solutions for UC/MC transitions in 5G core networks is presented and discussed. Using system-level simulation, the reliability of ML, MC and UC transmission in 5G core networks are investigated and discussed under different scenarios and environments. Firstly, when the threshold of ML switching is great (e.g., 10 dB), Modeling findings indicate that for the majority of user equipment (UE) density levels, ML consumes more resources than UC. This is due to the fact that ML employs extra PTP (Point-to-Point) connections. Secondly, if the UE density is more than 20 UEs/cell and the threshold is low (e.g., 5 dB), resource usage with ML will be lower than with UC since fewer UEs are using extra PTP lines. As a result, In the case of ML, expanding the UE density increases the number of PTP links, resulting in increased resource consumption and reduced Average layer spectral efficiency (AL-SE). When the UE density increases due to the MCS index limit and ML switching threshold, ML follows the same QoE evolution as UC with a worse Mean Opinion Score (MOS) score. Thirdly, Lowering of the ML switching threshold causes a reduction in the number of UEs with excellent service reception as more UEs have to rely on pure MC. Although still insufficient for the UE density of 20 UEs/cell or more, the active network resources can better handle the UEs because there are fewer duplicate PTP lines. If the density is equal to 10 UEs/cell and the threshold of ML switching is equal to 5 dB, when compared to UC, ML achieves ideal QoS for roughly 70% of the UEs, where more than 90% of the coverage is obtained. However, ML achieves an AL-SE that is nearly 50% greater than that in UC. The obtained results indicate that the MC delivery mode can be used as a network resource usage optimization for the distribution of general data. Moreover, through the use of ML technology, the trade-off in the reliability in service delivery and user experience by switching from UC to MC is improved. Hence, the ML is considered to be beneficial for intermediate UE density in the cell. For example, by using ML for most UEs, the reliability of the service delivery can be restored, and spectral efficiency is significantly improved compared to that using UC. To cover larger populations, different ML algorithms that are not as much resource demanding could be used. Therefore, lower threshold of ML switching and a greater modulation and coding scheme (MCS) index may be selected to bring more benefits to ML in terms of resource consumption.