  Ultra-massive multiple-input multiple-output (UMMIMO) is a cutting-edge
technology that promises to revolutionize wireless networks by providing an
unprecedentedly high spectral and energy efficiency. The enlarged array
aperture of UM-MIMO facilitates the accessibility of the near-field region,
thereby offering a novel degree of freedom for communications and sensing.
Nevertheless, the transceiver design for such systems is challenging because of
the enormous system scale, the complicated channel characteristics, and the
uncertainties of the propagation environments. Hence, it is critical to study
scalable, low-complexity, and robust algorithms that can efficiently
characterize and leverage the properties of the near-field channel. In this
article, we advocate two general frameworks from an artificial intelligence
(AI)-native perspective to design iterative and noniterative algorithms for the
near-field UM-MIMO transceivers, respectively. Near-field beam focusing and
channel estimation are presented as two tutorial-style examples to demonstrate
the significant advantages of the proposed AI-native frameworks in terms of
various key performance indicators.
