Extremely large-scale multiple-input-multiple-out-put (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, and so on. 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.

中文翻译：

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近场通信的资源分配：基础知识、工具和展望


超大规模多输入多输出 （XL-MIMO） 是一项很有前途的技术，可以在未来的无线系统中实现高频谱效率 （SE） 和能效 （EE）。XL-MIMO 更大的阵列孔径使通信场景更接近近场区域。因此，近场资源分配对于实现上述关键绩效指标 （KPI） 至关重要。此外，XL-MIMO 系统的整体性能在很大程度上取决于所选用户的信道特性，通过波束成形、功率控制等消除用户之间的干扰。上述资源分配问题构成了一个复杂的联合多目标优化问题，因为必须优化许多变量和参数，包括空间自由度、速率、功率分配和传输技术。在本文中，我们回顾了近场通信的基本特性，并重点介绍了相应的 “资源分配” 问题。首先，我们确定了近场通信系统中的可用资源，并强调了它们与远场通信的区别。然后，我们总结了用于解决近场资源分配的优化工具，例如数值技术和机器学习方法，强调了它们的优势和局限性。最后，指出了近场通信的几个重要研究方向，以供进一步研究。