Semantic communication (SemCom) leveraging advanced deep learning (DL) technologies enhances the efficiency and reliability of information transmission. Emerging stacked intelligent metasurface (SIM) with an electromagnetic neural network (EMNN) architecture enables complex computations at the speed of light. In this letter, we introduce an innovative SIM-aided SemCom system for image recognition tasks, where a SIM is positioned in front of the transmitting antenna. In contrast to conventional communication systems that transmit modulated signals carrying the image information or compressed semantic information, the carrier EM wave is directly transmitted from the source. The input layer of the SIM performs source encoding, while the remaining multi-layer architecture constitutes an EMNN for semantic encoding, transforming signals into a unique beam towards a receiving antenna corresponding to the image class. Remarkably, both the source and semantic encoding occur naturally as the EM waves propagate through the SIM. At the receiver, the image is recognized by probing the received signal magnitude across the receiving array. To this end, we utilize an efficient mini-batch gradient descent algorithm to train the transmission coefficients of SIM's meta-atoms to learn the semantic representation of the image. Extensive numerical results verify the effectiveness of utilizing the SIM-based EMNN for image recognition task-oriented SemComs, achieving more than 90\% recognition accuracy.

翻译：利用先进深度学习技术的语义通信提升了信息传输的效率和可靠性。新兴的堆叠智能超表面具有电磁神经网络架构，能够以光速执行复杂计算。本文提出了一种创新的、用于图像识别任务的SIM辅助语义通信系统，其中SIM被部署在发射天线前方。与传统通信系统传输携带图像信息或压缩语义信息的调制信号不同，该系统直接发射来自信源的载波电磁波。SIM的输入层执行信源编码，而其余多层架构则构成用于语义编码的电磁神经网络，将信号转换为指向对应图像类别的接收天线的独特波束。值得注意的是，信源编码和语义编码均在电磁波传播通过SIM的过程中自然完成。在接收端，通过探测接收阵列上的接收信号幅度来识别图像。为此，我们采用了一种高效的小批量梯度下降算法来训练SIM超原子的传输系数，以学习图像的语义表示。大量的数值结果验证了基于SIM的电磁神经网络在面向图像识别任务的语义通信中的有效性，其识别准确率超过90%。