The low-altitude economy (LAE) is an emerging economic paradigm which fosters integrated development across multiple fields. As a pivotal component of the LAE, low-altitude uncrewed aerial vehicles (UAVs) can restore communication by serving as aerial relays between the post-disaster areas and remote base stations (BSs). However, conventional approaches face challenges from vulnerable long-distance links between the UAVs and remote BSs, and data bottlenecks arising from massive data volumes and limited onboard UAV resources. In this work, we investigate a low-altitude multi-UAV-assisted data collection and semantic forwarding network, in which multiple UAVs collect data from ground users, form clusters, perform intra-cluster data aggregation with semantic extraction, and then cooperate as virtual antenna array (VAAs) to transmit the extracted semantic information to a remote BS via collaborative beamforming (CB). We formulate a data collection and semantic forwarding multi-objective optimization problem (DCSFMOP) that jointly maximizes both the user and semantic transmission rates while minimizing UAV energy consumption. The formulated DCSFMOP is a mixed-integer nonlinear programming (MINLP) problem that is inherently NP-hard and characterized by dynamically varying decision variable dimensionality. To address these challenges, we propose a large language model-enabled alternating optimization approach (LLM-AOA), which effectively handles the complex search space and variable dimensionality by optimizing different subsets of decision variables through tailored optimization strategies. Simulation results demonstrate that LLM-AOA outperforms AOA by approximately 26.8\% and 22.9\% in transmission rate and semantic rate, respectively.

翻译：低空经济是一种新兴的经济范式，其促进了多领域的融合发展。作为低空经济的关键组成部分，低空无人驾驶飞行器可通过充当灾后区域与远程基站之间的空中中继来恢复通信。然而，传统方法面临无人机与远程基站之间脆弱的长距离链路挑战，以及海量数据与有限机载无人机资源所导致的数据瓶颈。本文研究了一种低空多无人机辅助的数据收集与语义转发网络，其中多架无人机从地面用户收集数据，形成集群，通过语义提取进行集群内数据聚合，然后协作作为虚拟天线阵列，通过协作波束成形将提取的语义信息传输至远程基站。我们构建了一个数据收集与语义转发多目标优化问题，旨在联合最大化用户传输速率与语义传输速率，同时最小化无人机能耗。该问题是一个混合整数非线性规划问题，本质上是NP难问题，且具有决策变量维度动态变化的特征。为应对这些挑战，我们提出了一种基于大语言模型的交替优化方法，该方法通过针对性的优化策略优化不同决策变量子集，有效处理了复杂的搜索空间和变量维度问题。仿真结果表明，在传输速率和语义速率方面，所提方法分别比传统交替优化方法提升了约26.8%和22.9%。