  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.
