Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG
<|reference_start|>Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG: 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.<|reference_end|>
@article{xu2024learning,
    title={Learning at the Speed of Wireless: Online Real-Time Learning for
  AI-Enabled MIMO in NextG},
    author={Jiarui Xu, Shashank Jere, Yifei Song, Yi-Hung Kao, Lizhong Zheng, and
  Lingjia Liu},
    journal={arXiv preprint arXiv:2403.02651},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02651},
    primaryClass={eess.SP cs.AI}
}