分布式电磁神经网络用于面向任务的语义通信
Distributed Electromagnetic Neural Networks for Task-Oriented Semantic Communications
AI总结：
本文提出基于无人机的分布式电磁神经网络，通过多智能超材料协同编码图像语义，提升语义通信效率，实现平均8%的准确率提升。
摘要：
语义通信（SemCom）是一种优先传输任务相关信息的有前景范式，从而在传统比特中心系统上实现更高效的通信效率。然而，现有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，我们提出了一种新的无人机（UAV）赋能的分布式电磁神经网络（EMNN）用于面向任务的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材料（SIM）和地面接收站（GRS）组成，其中多个SIM协同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，以缓解梯度消失并增强学习稳定性。最后，数值模拟结果证明了分布式EMNN在面向图像识别任务的SemCom中的有效性，实现了在多个数据集上的单SIM基线的平均8%的准确率提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
