Wireless communication systems face challenges in meeting the demand for higher data rates and reliable connectivity in complex environments. Stacked intelligent metasurfaces (SIMs) have emerged as a promising technology for advanced wave-domain signal processing, where mobile SIMs can outperform fixed counterparts. In this paper, we propose a novel unmanned aerial vehicle (UAV)-mounted SIM (UAV-SIM) assisted communication system within low-altitude economy (LAE) networks, where UAVs act as both cache-enabled base stations and mobile SIM carriers to enhance uplink transmissions. To maximize network capacity, we formulate a UAV-SIM-based joint optimization problem (USBJOP) that integrates user association, UAV-SIM three-dimensional positioning, and multi-layer SIM phase shift design. Due to the non-convexity and NP-hardness of USBJOP, we decompose it into three subproblems, which are the association between UAV-SIMs and users optimization problem (AUUOP), the UAV location optimization problem (ULOP), and the UAV-SIM phase shifts optimization problem (USPSOP). Then, we solve them through an alternating optimization strategy. Specifically, AUUOP and ULOP are transformed into convex forms solvable via the CVX tool, while USPSOP is addressed by a generative artificial intelligence (GAI)-based hybrid optimization algorithm. Simulation results show that the proposed approach achieves approximately 1.5 times higher network capacity compared with suboptimal schemes, effectively mitigates multi-user interference with increasing SIM layers and meta-atoms, and reduces runtime by 10\% while maintaining solution quality, thereby demonstrating its practicality for real-world deployments.

翻译：无线通信系统在复杂环境中面临着满足更高数据速率和可靠连接需求的挑战。叠层智能超表面（SIMs）作为一种先进的波域信号处理技术应运而生，其中移动式SIMs的性能可超越固定式。本文提出了一种低空经济（LAE）网络中的新型无人机载SIM（UAV-SIM）辅助通信系统，其中无人机既作为具备缓存功能的基站，也作为移动SIM载体，以增强上行链路传输。为了最大化网络容量，我们构建了一个基于UAV-SIM的联合优化问题（USBJOP），该问题整合了用户关联、UAV-SIM三维定位以及多层SIM相移设计。鉴于USBJOP的非凸性和NP难特性，我们将其分解为三个子问题，即UAV-SIM与用户关联优化问题（AUUOP）、无人机位置优化问题（ULOP）以及UAV-SIM相移优化问题（USPSOP）。随后，通过交替优化策略对它们进行求解。具体而言，AUUOP和ULOP被转化为可通过CVX工具求解的凸形式，而USPSOP则通过一种基于生成式人工智能（GAI）的混合优化算法进行处理。仿真结果表明，与次优方案相比，所提方法实现了约1.5倍的网络容量提升，随着SIM层数和超原子数量的增加能有效缓解多用户干扰，并在保持解质量的同时将运行时间减少了10%，从而证明了其在现实部署中的实用性。