论文标题
人工智能使Noma能够进入下一代多访问
Artificial Intelligence Enabled NOMA Towards Next Generation Multiple Access
论文作者
论文摘要
本文重点介绍了人工智能（AI）在非正交多访问（NOMA）中的应用，该文章旨在将自动化，适应性和高效的多用户通信用于下一代多重访问（NGMA）。首先，讨论了当前场景特异性多种抗NOMA方案的局限性，并突出了AI对NGMA的重要性。然后，为了实现NGMA的愿景，提出了一个新型的无簇NOMA框架，以提供情景自适应的Noma通信，并确定了几种有希望的机器学习解决方案。为了进一步详细说明，新颖的集中和分布式机器学习范例是为了在单细胞和多细胞网络中有效地采用所提出的无聚类的Noma框架，在此提供数值结果以证明有效性。此外，提出了所提出的无聚类Noma和新兴无线技术之间的相互作用。最后，讨论了一些启用AI的NGMA的开放研究问题。
This article focuses on the application of artificial intelligence (AI) in non-orthogonal multiple-access (NOMA), which aims to achieve automated, adaptive, and high-efficiency multi-user communications towards next generation multiple access (NGMA). First, the limitations of current scenario-specific multiple-antenna NOMA schemes are discussed, and the importance of AI for NGMA is highlighted. Then, to achieve the vision of NGMA, a novel cluster-free NOMA framework is proposed for providing scenario-adaptive NOMA communications, and several promising machine learning solutions are identified. To elaborate further, novel centralized and distributed machine learning paradigms are conceived for efficiently employing the proposed cluster-free NOMA framework in single-cell and multi-cell networks, where numerical results are provided to demonstrate the effectiveness. Furthermore, the interplays between the proposed cluster-free NOMA and emerging wireless techniques are presented. Finally, several open research issues of AI enabled NGMA are discussed.
