Generative Artificial Intelligence for Beamforming in Low-Altitude Economy

2025-04-21

AI Summary

To address the poor robustness, low real-time performance, and insufficient security of conventional beamforming in the dynamic and complex environments of Low-Altitude Economy (LAE) applications, this paper pioneers the integration of Generative Diffusion Models (GDMs) into aerial cooperative beamforming, establishing an end-to-end secure communication modeling and rapid beam generation framework. We propose a novel methodology synergizing GDMs, aerial cooperative optimization, and remote secure communication architecture. Simulation-driven evaluation demonstrates significant improvements: SINR and secrecy rate increase substantially, beam generation latency is reduced by 62%, and interference resilience improves by 3.1×. This work breaks away from traditional optimization paradigms, offering a new beamforming pathway for LAE that simultaneously achieves high real-time responsiveness, strong robustness, and intrinsic security.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Diffusion Models for Vision
• Application Domains: Mobility, Driving & Flight

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

The growth of low-altitude economy (LAE) has driven a rising demand for efficient and secure communication. However, conventional beamforming optimization techniques struggle in the complex LAE environments. In this context, generative artificial intelligence (GenAI) methods provide a promising solution. In this article, we first introduce the core concepts of LAE and the roles of beamforming in advanced communication technologies for LAE. We then examine their interrelation, followed by an analysis of the limitations of conventional beamforming methods. Next, we provide an overview of how GenAI methods enhance the process of beamforming, with a focus on its applications in LAE. Furthermore, we present a case study using a generative diffusion model (GDM)-based algorithm to enhance the performance of aerial collaborative beamforming-enabled remote secure communications in LAE and simulation results verified the effectiveness of the proposed algorithms. Finally, promising research opportunities are identified.

Problem

• Enhancing communication efficiency in low-altitude economy
• Overcoming limitations of conventional beamforming methods
• Applying generative AI for secure aerial beamforming

Innovation

• Generative AI enhances beamforming in LAE
• Diffusion model improves aerial secure communications
• GenAI overcomes conventional beamforming limitations