Integration of artificial intelligence (AI) and machine learning (ML) into
the air interface has been envisioned as a key technology for next-generation
(NextG) cellular networks. At the air interface, multiple-input multiple-output
(MIMO) and its variants such as multi-user MIMO (MU-MIMO) and
massive/full-dimension MIMO have been key enablers across successive
generations of cellular networks with evolving complexity and design
challenges. Initiating active investigation into leveraging AI/ML tools to
address these challenges for MIMO becomes a critical step towards an AI-enabled
NextG air interface. At the NextG air interface, the underlying wireless
environment will be extremely dynamic with operation adaptations performed on a
sub-millisecond basis by MIMO operations such as MU-MIMO scheduling and
rank/link adaptation. Given the enormously large number of operation adaptation
possibilities, we contend that online real-time AI/ML-based approaches
constitute a promising paradigm. To this end, we outline the inherent
challenges and offer insights into the design of such online real-time
AI/ML-based solutions for MIMO operations. An online real-time AI/ML-based
method for MIMO-OFDM channel estimation is then presented, serving as a
potential roadmap for developing similar techniques across various MIMO
operations in NextG.

将人工智能（AI）和机器学习（ML）整合到空中接口中被视为下一代（NextG）蜂窝网络的关键技术之一。在空中接口中，多输入多输出（MIMO）及其变种，如多用户 MIMO（MU-MIMO）和巨大 / 全维 MIMO，在接连几代的蜂窝网络中成为关键的支持者，面对不断演进的复杂性和设计挑战。将 AI/ML 工具应用于解决 MIMO 中的这些挑战成为实现 AI 启用的 NextG 空中接口的关键步骤。在下一代空中接口中，底层的无线环境将极为动态，由 MIMO 操作（如 MU-MIMO 调度和等级 / 链接自适应）在次毫秒级别上进行操作调整。鉴于庞大的操作调整可能性，我们认为在线实时的基于 AI/ML 的方法具有潜在的前景。为此，我们概述了这类在线实时 AI/ML 基于解决方案设计中的固有挑战，并提供了关于这类解决方案在 MIMO 操作中的开发见解。随后，我们提出了一种基于在线实时 AI/ML 的 MIMO-OFDM 信道估计方法，作为在 NextG 中开发类似技术的潜在路线图。

无线速度的学习：面向下一代的 AI-Enabled MIMO 的在线实时学习

Learning at the Speed of Wireless: Online Real-Time Learning for  AI-Enabled MIMO in NextG

Each generation of cellular communication systems is marked by a defining
disruptive technology of its time, such as orthogonal frequency division
multiplexing (OFDM) for 4G or Massive multiple-input multiple-output (MIMO) for
5G. Since artificial intelligence (AI) is the defining technology of our time,
it is natural to ask what role it could play for 6G. While it is clear that 6G
must cater to the needs of large distributed learning systems, it is less
certain if AI will play a defining role in the design of 6G itself. The goal of
this article is to paint a vision of a new air interface which is partially
designed by AI to enable optimized communication schemes for any hardware,
radio environment, and application.

论文探讨利用人工智能为 6G 设计部分新的空中界面，来使得其能够为任意硬件、无线环境和应用程序实现优化的通信方案。