Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG
This addresses the need for efficient, real-time AI solutions in next-generation wireless networks, but it appears incremental as it builds on existing MIMO and AI/ML paradigms.
The paper tackles the challenge of integrating AI/ML into MIMO operations for NextG cellular networks, which require sub-millisecond adaptations in dynamic wireless environments, by proposing an online real-time AI/ML-based method for MIMO-OFDM channel estimation as a roadmap for broader applications.
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.