Nonorthogonal Multiple Access and Subgrouping for Improved Resource Allocation in Multicast 5G NR

Abstract

The ever-increasing demand for applications with stringent constraints in device density, latency, user mobility, or peak data rate has led to the appearance of the last generation of mobile networks (i.e., 5G). However, there is still room for improvement in the network spectral efficiency, not only at the waveform level but also at the Radio Resource Management (RRM). Up to now, solutions based on multicast transmissions have presented considerable efficiency increments by successfully implementing subgrouping strategies. These techniques enable more efficient exploitation of channel time and frequency resources by splitting users into subgroups and applying independent and adaptive modulation and coding schemes. However, at the RRM, traditional multiplexing techniques pose a hard limit in exploiting the available resources, especially when users' QoS requests are unbalanced. Under these circumstances, this paper proposes jointly applying the subgrouping and Non-Orthogonal Multiple Access (NOMA) techniques in 5G to increase the network data rate. This study shows that NOMA is highly spectrum-efficient and could improve the system throughput performance in certain conditions. In the first part of this paper, an in-depth analysis of the implications of introducing NOMA techniques in 5G subgrouping at RRM is carried out. Afterward, the validation is accomplished by applying the proposed approach to different 5G use cases based on vehicular communications. After a comprehensive analysis of the results, a theoretical approach combining NOMA and time division is presented, which improves considerably the data rate offered in each use case.This work was supported in part by the Italian Ministry of University and Research (MIUR), within the Smart Cities framework, Project Cagliari2020 ID: PON04a2_00381; in part by the Basque Government under Grant IT1234-19; and in part by the Spanish Government [Project PHANTOM under Grant RTI2018-099162-B-I00 (MCIU/AEI/FEDER, UE)]

    Similar works