Semantic communication leveraging advanced deep learning (DL) technologies enhances the efficiency, reliability, and security of information transmission. Emerging stacked intelligent metasurface (SIM) having a diffractive neural network (DNN) architecture allows performing complex calculations at the speed of light. In this letter, we introduce an innovative SIM-aided semantic communication system for image recognition tasks. In the considered model, a SIM is positioned in front of the transmitting antenna. In contrast to conventional communication systems transmitting the modulated signals carrying the image information or compressed semantic information, the carrier electromagnetic (EM) wave is directly transmitted from the source in the proposed system. The input layer of the SIM is utilized for source encoding, while the remaining multi-layer architecture constitutes a DNN for semantic encoding. Specifically, the semantic encoder aims to transform the signals passing through the input layer of the SIM 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 develop an efficient 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 DNN for image recognition task-oriented semantic communications, 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、可靠性与安全性。具有衍射神经网络架构的新兴堆叠智能超表面能够在光速下执行复杂计算。本文提出一种创新的SIM辅助语义通信系统用于图像识别任务。在所考虑的模型中，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，本系统直接由信源发射载波电磁波。SIM的输入层被用于信源编码，而其余多层架构则构成用于语义编码的DNN。具体而言，语义编码器的目标是将通过SIM输入层的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信号幅度实现图像识别。为此，我们开发了一种高效算法来训练SIM超原子的传输系数，以学习图像的语义表征。大量数值结果验证了基于SIM的DNN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%。