Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning, e.g., deep learning, provide promising approaches to deal with complex and previously intractable problems. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed.

翻译：迄今为止，无线通信系统主要依赖资源正交性来简化从用户接入到数据传输的设计与实现。第六代（6G）无线系统中涌现的新应用与场景将需要海量连接和巨量数据传输，这要求设计理念超越正交性，具备更高的灵活性。此外，信号处理与学习领域的最新进展（例如深度学习）为处理复杂且以往难以解决的问题提供了有前景的方法。本文概述了迄今为止在面向下一代多址接入的信号处理与学习领域的研究成果，重点聚焦于大规模随机接入与非正交多址接入。文中讨论了与新兴技术的有前景的融合，以及基于学习的下一代多址接入所面临的挑战。