  Extremely large-scale multiple-input multiple-output (XL-MIMO) is capable of
supporting extremely high system capacities with large numbers of users. In
this work, we build a framework for the analysis and low-complexity design of
XL-MIMO in the near field with spatial non-stationarities. Specifically, we
first analyze the theoretical performance of discrete-aperture XL-MIMO using an
electromagnetic (EM) channel model based on the near-field spherical wavefront.
We analytically reveal the impact of the discrete aperture and polarization
mismatch on the received power. We also complement the classical Fraunhofer
distance based on the considered EM channel model. Our analytical results
indicate that a limited part of the XL-array receives the majority of the
signal power in the near field, which leads to a notion of visibility region
(VR) of a user. Thus, we propose a VR detection algorithm and leverage the
acquired VR information to devise a low-complexity symbol detection scheme.
Furthermore, we propose a graph theory-based user partition algorithm, relying
on the VR overlap ratio between different users. Partial zero-forcing (PZF) is
utilized to eliminate only the interference from users allocated to the same
group, which further reduces computational complexity in matrix inversion.
Numerical results confirm the correctness of the analytical results and the
effectiveness of the proposed algorithms. It reveals that our algorithms
approach the performance of conventional whole array (WA)-based designs but
with much lower complexity.
