  Extremely large-scale multiple-input-multiple output (XL-MIMO) is a promising
technology to achieve high spectral efficiency (SE) and energy efficiency (EE)
in future wireless systems. The larger array aperture of XL-MIMO makes
communication scenarios closer to the near-field region. Therefore, near-field
resource allocation is essential in realizing the above key performance
indicators (KPIs). Moreover, the overall performance of XL-MIMO systems heavily
depends on the channel characteristics of the selected users, eliminating
interference between users through beamforming, power control, etc. The above
resource allocation issue constitutes a complex joint multi-objective
optimization problem since many variables and parameters must be optimized,
including the spatial degree of freedom, rate, power allocation, and
transmission technique. In this article, we review the basic properties of
near-field communications and focus on the corresponding "resource allocation"
problems. First, we identify available resources in near-field communication
systems and highlight their distinctions from far-field communications. Then,
we summarize optimization tools, such as numerical techniques and machine
learning methods, for addressing near-field resource allocation, emphasizing
their strengths and limitations. Finally, several important research directions
of near-field communications are pointed out for further investigation.
