Generative AI-enhanced Low-Altitude UAV-Mounted Stacked Intelligent Metasurfaces

2025-06-29

AI Summary

To address the low uplink capacity and poor reliability of ground users in low-altitude economy scenarios, this paper proposes a UAV-mounted stacked intelligent metasurface (UAV-SIMs) enhancement architecture. We formulate a non-convex joint optimization problem encompassing user association, three-dimensional UAV deployment, and multi-layer metasurface phase control. To tackle the high-dimensional non-convex phase optimization, we innovatively integrate generative AI into the solution framework and design a CVX-assisted hybrid alternating optimization algorithm. Compared to benchmark schemes, the proposed approach achieves approximately a 1.5× improvement in network capacity, reduces computational runtime by 10%, and simultaneously ensures communication reliability while significantly enhancing solution efficiency and scalability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Learning & Optimization for CV
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Wireless communication systems face significant challenges in meeting the increasing demands for higher data rates and more reliable connectivity in complex environments. Stacked intelligent metasurfaces (SIMs) have emerged as a promising technology for realizing wave-domain signal processing, with mobile SIMs offering superior communication performance compared to their fixed counterparts. In this paper, we investigate a novel unmanned aerial vehicle (UAV)-mounted SIMs (UAV-SIMs) assisted communication system within the low-altitude economy (LAE) networks paradigm, where UAVs function as both base stations that cache SIM-processed data and mobile platforms that flexibly deploy SIMs to enhance uplink communications from ground users. To maximize network capacity, we formulate a UAV-SIM-based joint optimization problem (USBJOP) that comprehensively addresses three critical aspects: the association between UAV-SIMs and users, the three-dimensional positioning of UAV-SIMs, and the phase shifts across multiple SIM layers. Due to the inherent non-convexity and NP-hardness of USBJOP, we decompose it into three sub-optimization problems, extit{i.e.}, association between UAV-SIMs and users optimization problem (AUUOP), UAV location optimization problem (ULOP), and UAV-SIM phase shifts optimization problem (USPSOP), and solve them using an alternating optimization strategy. Specifically, we transform AUUOP and ULOP into convex forms solvable by the CVX tool, while addressing USPSOP through a generative artificial intelligence (GAI)-based hybrid optimization algorithm. Simulations demonstrate that our proposed approach significantly outperforms benchmark schemes, achieving approximately 1.5 times higher network capacity compared to suboptimal alternatives. Additionally, our proposed GAI method reduces the algorithm runtime by 10% while maintaining solution quality.

Problem

• Enhancing uplink communications in low-altitude UAV networks
• Optimizing UAV-SIM positioning and user association for capacity
• Solving non-convex phase shift optimization via generative AI

Innovation

• UAV-mounted stacked intelligent metasurfaces for communication
• Generative AI hybrid optimization for phase shifts
• Alternating optimization strategy for network capacity