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Ultra-massive multiple-input multiple-output (UMMIMO) is a cutting-edgetechnology that promises to revolutionize wireless networks by providing anunprecedentedly high spectral and energy efficiency. The enlarged arrayaperture 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 ofthe enormous system scale, the complicated channel characteristics, and theuncertainties of the propagation environments. Hence, it is critical to studyscalable, low-complexity, and robust algorithms that can efficientlycharacterize and leverage the properties of the near-field channel. In thisarticle, we advocate two general frameworks from an artificial intelligence(AI)-native perspective to design iterative and noniterative algorithms for thenear-field UM-MIMO transceivers, respectively. Near-field beam focusing andchannel estimation are presented as two tutorial-style examples to demonstratethe significant advantages of the proposed AI-native frameworks in terms ofvarious key performance indicators.

AI-Native Transceiver Design for Near-Field Ultra-Massive MIMO: Principles and Techniques