[Paper Review] Evolution of NOMA Toward Next Generation Multiple Access (NGMA).

Yuanwei Liu, Shuowen Zhang+6 more|arXiv (Cornell University)|Aug 10, 2021

Advanced Wireless Communication Technologies234 references8 citations

TL;DR

This paper proposes a unified framework for next-generation multiple access (NGMA) based on non-orthogonal multiple access (NOMA) and multiple antennas, integrating downlink and uplink transmission. It advances NGMA by analyzing NOMA’s capacity limits, exploring candidate techniques, and leveraging optimization and machine learning to enable massive connectivity with low latency and high spectral efficiency.

ABSTRACT

Due to the explosive growth in the number of wireless devices and diverse wireless services, such as virtual/augmented reality and Internet-of-Everything, next generation wireless networks face unprecedented challenges caused by heterogeneous data traffic, massive connectivity, and ultra-high bandwidth efficiency and ultra-low latency requirements. To address these challenges, advanced multiple access schemes are expected to be developed, namely next generation multiple access (NGMA), which are capable of supporting massive numbers of users in a more resource- and complexity-efficient manner than existing multiple access schemes. As the research on NGMA is in a very early stage, in this paper, we explore the evolution of NGMA with a particular focus on non-orthogonal multiple access (NOMA), i.e., the transition from NOMA to NGMA. In particular, we first review the fundamental capacity limits of NOMA, elaborate the new requirements for NGMA, and discuss several possible candidate techniques. Moreover, given the high compatibility and flexibility of NOMA, we provide an overview of current research efforts on multi-antenna techniques for NOMA, promising future application scenarios of NOMA, and the interplay between NOMA and other emerging physical layer techniques. Furthermore, we discuss advanced mathematical tools for facilitating the design of NOMA communication systems, including conventional optimization approaches and new machine learning techniques. Next, we propose a unified framework for NGMA based on multiple antennas and NOMA, where both downlink and uplink transmission are considered, thus setting the foundation for this emerging research area. Finally, several practical implementation challenges for NGMA are highlighted as motivation for future work.

Motivation & Objective

• Address the challenges of massive connectivity, ultra-low latency, and high spectral efficiency in next-generation wireless networks driven by emerging services like IoT and AR/VR.
• Identify the limitations of existing multiple access schemes in supporting heterogeneous traffic and massive device connectivity.
• Propose a unified NGMA framework integrating NOMA and multiple antennas to enable efficient downlink and uplink transmission.
• Explore candidate physical layer techniques and mathematical tools, including optimization and machine learning, to enhance NGMA system design.
• Highlight practical implementation challenges to guide future research in NGMA deployment and standardization.

Proposed method

• Review the fundamental capacity limits of NOMA to establish a foundation for NGMA evolution.
• Analyze new requirements for NGMA, including support for massive connectivity, low latency, and high spectral efficiency.
• Examine multi-antenna techniques for NOMA, emphasizing beamforming and spatial multiplexing gains.
• Investigate the interplay between NOMA and emerging physical layer techniques such as intelligent reflecting surfaces and terahertz communications.
• Integrate conventional optimization methods and machine learning techniques to design robust and adaptive NOMA systems.
• Propose a unified NGMA framework combining NOMA and multiple antennas, supporting both downlink and uplink transmission with scalable resource allocation.

Experimental results

Research questions

• RQ1 How can NOMA evolve into a scalable and efficient foundation for next-generation multiple access (NGMA) in future wireless networks?
• RQ2 What physical layer techniques are most promising for enhancing NGMA performance in terms of spectral efficiency and user fairness?
• RQ3 How can multiple antenna systems be effectively integrated with NOMA to support massive connectivity and low-latency services?
• RQ4 What role do advanced mathematical tools like optimization and machine learning play in enabling practical NGMA system design?
• RQ5 What are the key implementation challenges that must be addressed for NGMA to transition from theory to real-world deployment?

Key findings

• The paper establishes that NOMA provides a high-complexity, high-spectral-efficiency foundation for NGMA, enabling simultaneous user multiplexing through power-domain superposition.
• Multi-antenna techniques significantly enhance NOMA performance by improving user separation and interference management in both downlink and uplink scenarios.
• The integration of NOMA with emerging physical layer technologies such as intelligent reflecting surfaces and terahertz communications shows strong potential for future NGMA systems.
• Machine learning techniques offer a promising path for optimizing NOMA resource allocation and user pairing, especially in dynamic and heterogeneous environments.
• The proposed unified NGMA framework based on NOMA and multiple antennas provides a scalable and flexible architecture for supporting massive connectivity and ultra-reliable low-latency communication.
• Practical challenges such as hardware impairments, channel estimation accuracy, and feedback overhead are identified as critical barriers to NGMA deployment, requiring further research.