Semantic communications (SemCom) is a promising paradigm that prioritizes the transmission of task-relevant information, thereby enabling superior communication efficiency over traditional bit-centric systems. However, most existing SemCom systems face critical limitations in computational efficiency and spatial flexibility. To overcome these limitations, we propose a novel unmanned aerial vehicles (UAV)-enabled distributed electromagnetic neural network (EMNN) for a task-oriented SemCom system. Specifically, the proposed distributed EMNN is composed of multiple UAV-mounted stacked intelligent metasurfaces (SIM) and a ground receiving station (GRS), where multiple SIMs collaboratively encode image semantics in the wave domain, and the GRS performs decoding based on the received power distribution. Moreover, we employ a temperature-adaptive gradient optimization algorithm to train the distributed EMNN, which mitigates gradient vanishing and enhances learning stability. Finally, the numerical simulation results demonstrate the effectiveness of distributed EMNN in image recognition task-oriented SemCom, achieving an average $8\%$ accuracy improvement over the single-SIM baseline across multiple datasets.

翻译：语义通信（SemCom）是一种优先传输任务相关信息的新兴范式，能够实现比传统比特中心系统更优的通信效率。然而，现有大多数SemCom系统在计算效率和空间灵活性方面面临关键限制。为克服这些限制，我们提出了一种新型无人机（UAV）支持的分布式电磁神经网络（EMNN），用于任务导向的SemCom系统。具体而言，所提出的分布式EMNN由多个无人机搭载的堆叠智能超表面（SIM）和一个地面接收站（GRS）组成，其中多个SIM协作在波域中编码图像语义，而GRS基于接收到的功率分布进行解码。此外，我们采用温度自适应梯度优化算法来训练分布式EMNN，该方法缓解了梯度消失问题并增强了学习稳定性。最后，数值仿真结果证明了分布式EMNN在图像识别任务导向SemCom中的有效性，在多个数据集上相比单SIM基线实现了平均8%的准确率提升。