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。