NOMA as the Next-Generation Multiple Access in Nonterrestrial Networks

Abstract

Nonterrestrial networks (NTN) are pivotal, enabling technologies for achieving global and ubiquitous connectivity in the sixth generation (6G) of wireless systems. On the other hand, nonorthogonal multiple access (NOMA) emerges as a promising candidate for the next-generation multiple access (NGMA), designed to enable massive connectivity and improve spectral efficiency. In this article, we investigate the synergy between NTN and NOMA [power-domain NOMA (PD-NOMA)], which are integral in addressing the connectivity challenges of 6G. First, we present an overview of NTN, detailing their types, unique characteristics, and the challenges they face. Then, we explain the foundational principles of NOMA, such as power allocation and access strategies. We, then, argue the suitability of NOMA as an NGMA technology for 6G and how it addresses the specific challenges associated with NTN. Furthermore, we explore the integration and the interplay of NTN and NOMA with emerging technologies such as millimeter-wave (mmWave), terahertz (THz) frequencies, reconfigurable intelligent surfaces (RISs), and integrated sensing and communication (ISAC) systems. These advancements are critical in meeting the 6G requirements for extremely high data rates and improved spectral efficiency, enhancing the overall functionality of NTN and NOMA. We, then, examine how NTN using NOMA can use different architectural frameworks, including cellular, cell-free (CF), ad hoc, and integrated access and backhaul (IAB) systems, and detailed several use cases where the integration of NTN and NOMA could have a significant impact, such as in vehicular communications, the Internet of Things (IoT), urban air mobility (UAM), and achieving global connectivity. To optimize NTN using NOMA, we present a comprehensive overview of the mathematical optimization algorithms and machine learning tools. Finally, we elaborate on the primary challenges associated with merging NTN with NOMA and propose relevant future research avenues.