Empowering Near-Field Communications in Low-Altitude Economy with LLM: Fundamentals, Potentials, Solutions, and Future Directions

2025-06-20

AI Summary

To address the challenges of low beam focusing accuracy, ambiguity between far-field and near-field users, and high signal processing complexity in near-field communications for low-altitude economy applications, this paper proposes, for the first time, an LLM-integrated physical-layer design for ultra-massive MIMO systems operating in the near-field regime. The proposed approach jointly performs user identification and multi-user precoding by synergistically combining near-field electromagnetic modeling, XL-MIMO beam focusing, end-to-end channel semantic understanding, and large language model (LLM) reasoning capabilities—thereby overcoming the generalization limitations of conventional model-driven methods. Experimental results demonstrate a 37% reduction in computational complexity, a 21% improvement in near-field user localization accuracy, and a far-field/near-field user classification accuracy exceeding 98.4%.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Enhancing near-field communications in low-altitude economy using LLM
• Addressing signal processing complexity in near-field LAE communications
• Distinguishing far and near-field users with LLM solutions

Innovation

• LLM-based scheme for near-field communications
• Joint far and near-field user distinction
• Multi-user precoding matrix design