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）是提升未来无线系统频谱效率和能量效率的关键技术。由于XL-MIMO的大孔径阵列使通信场景更接近近场区域，近场资源配置成为实现上述关键性能指标的核心环节。同时，XL-MIMO系统整体性能高度依赖于所选用户的信道特性，需要通过波束成形、功率控制等手段消除用户间干扰。上述资源配置问题涉及空间自由度、速率、功率分配和传输技术等多变量参数优化，构成复杂的多目标联合优化难题。本文系统梳理了近场通信的基础特性，着重分析其对应的"资源配置"问题。首先，我们识别了近场通信系统中的可用资源，并阐明其与远场通信的本质差异；其次，综述了数值优化与机器学习方法等解决近场资源配置的技术工具，重点剖析各类方法的优势与局限性；最后，展望了近场通信领域亟待突破的若干重要研究方向。