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.

翻译：超大规模多输入多输出（XL-MIMO）是未来无线系统中实现高频谱效率（SE）和能量效率（EE）的关键技术。XL-MIMO更大的阵列孔径使得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性能指标（KPI）至关重要。此外，XL-MIMO系统的整体性能在很大程度上依赖于所选用户的信道特性，通过波束赋形、功率控制等手段消除用户间干扰。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，因为需要优化众多变量和参数，包括空间自由度、速率、功率分配和传输技术等。本文综述了近场通信的基本特性，并重点探讨相应的"资源分配"问题。首先，我们识别了近场通信系统中的可用资源，并强调其与远场通信的区别。随后，我们总结了解决近场资源分配的优化工具（如数值技术和机器学习方法），并重点分析其优势与局限性。最后，我们指出了近场通信的几个重要研究方向以供进一步探索。