用大模型解决低空经济中近场通信的复杂难题
Empowering Near-Field Communications in Low-Altitude Economy with LLM: Fundamentals, Potentials, Solutions, and Future Directions
- 用大语言模型处理近场通信的信号复杂性
- 实现远近用户区分与多用户预编码联合设计
- 适合研究智能通信与低空网络的学者
低空经济（LAE）正受到学术界和产业界的广泛关注。由于其天然契合超大规模MIMO（XL-MIMO）系统的近场通信，可通过近场波束聚焦精准定向无人机能量，同时利用额外的距离维度提升频谱效率。然而，近场通信在LAE中仍面临信号处理复杂度上升、远近场用户难以区分等挑战。受大语言模型（LLM）处理复杂问题能力的启发，本文提出将LLM应用于解决这些挑战。文章系统分析了LLM与近场通信的基本原理，揭示了其在LAE中的机遇与挑战，并提出一种基于LLM的近场通信方案。通过案例研究，实现了远近场用户联合识别与多用户预编码矩阵设计。最后，梳理了未来关键研究方向与开放问题。
原文摘要 · Abstract (English)
The low-altitude economy (LAE) is gaining significant attention from academia and industry. Fortunately, LAE naturally aligns with near-field communications in extremely large-scale MIMO (XL-MIMO) systems. By leveraging near-field beamfocusing, LAE can precisely direct beam energy to unmanned aerial vehicles, while the additional distance dimension boosts overall spectrum efficiency. However, near-field communications in LAE still face several challenges, such as the increase in signal processing complexity and the necessity of distinguishing between far and near-field users. Inspired by the large language models (LLM) with powerful ability to handle complex problems, we apply LLM to solve challenges of near-field communications in LAE. The objective of this article is to provide a comprehensive analysis and discussion on LLM-empowered near-field communications in LAE. Specifically, we first introduce fundamentals of LLM and near-field communications, including the key advantages of LLM and key characteristics of near-field communications. Then, we reveal the opportunities and challenges of near-field communications in LAE. To address these challenges, we present a LLM-based scheme for near-field communications in LAE, and provide a case study which jointly distinguishes far and near-field users and designs multi-user precoding matrix. Finally, we outline and highlight several future research directions and open issues.
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